micromachines Review Role of Drone Technology Helping in Alleviating the COVID-19 Pandemic Syed Agha Hassnain Mohsan 1,* , Qurat ul Ain Zahra 2 , Muhammad Asghar Khan 3 , Mohammed H. Alsharif 4,* , Ismail A. Elhaty 5 and Abu Jahid 6 1 Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan 316021, China 2 Department of Biomedical Engineering, Biomedical Imaging Centre, University of Science and Technology of China, Hefei 230009, China 3 Department of Electrical Engineering, Hamdard Institute of Engineering & Technology, Islamabad 44000, Pakistan 4 Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Korea 5 Department of Nutrition and Dietetics, Faculty of Health Sciences, Istanbul Gelisim University, Istanbul P.O. Box 34310, Turkey 6 School of Electrical Engineering and Computer Science, University of Ottawa, 25 Templeton St., Ottawa, ON K1N 6N5, Canada * Correspondence: hassnainagha@zju.edu.cn (S.A.H.M.); malsharif@sejong.ac.kr (M.H.A.) Abstract: The COVID-19 pandemic, caused by a new coronavirus, has affected economic and social standards as governments and healthcare regulatory agencies throughout the world expressed worry and explored harsh preventative measures to counteract the disease’s spread and intensity. Several academics and experts are primarily concerned with halting the continuous spread of the unique virus. Social separation, the closing of borders, the avoidance of big gatherings, contactless transit, and quarantine are important methods. Multiple nations employ autonomous, digital, wireless, and Citation: Mohsan, S.A.H.; Zahra, other promising technologies to tackle this coronary pneumonia. This research examines a number Q.u.A.; Khan, M.A.; Alsharif, M.H.; of potential technologies, including unmanned aerial vehicles (UAVs), artificial intelligence (AI), Elhaty, I.A.; Jahid, A. Role of Drone blockchain, deep learning (DL), the Internet of Things (IoT), edge computing, and virtual reality Technology Helping in Alleviating (VR), in an effort to mitigate the danger of COVID-19. Due to their ability to transport food and the COVID-19 Pandemic. medical supplies to a specific location, UAVs are currently being utilized as an innovative method Micromachines 2022, 13, 1593. to combat this illness. This research intends to examine the possibilities of UAVs in the context of https://doi.org/10.3390/mi13101593 the COVID-19 pandemic from several angles. UAVs offer intriguing options for delivering medical Academic Editor: Alberto Corigliano supplies, spraying disinfectants, broadcasting communications, conducting surveillance, inspecting, and screening patients for infection. This article examines the use of drones in healthcare as well as Received: 21 August 2022 the advantages and disadvantages of strict adoption. Finally, challenges, opportunities, and future Accepted: 22 September 2022 work are discussed to assist in adopting drone technology to tackle COVID-19-like diseases. Published: 25 September 2022 Publisher’s Note: MDPI stays neutral Keywords: UAVs; COVID-19; technologies; healthcare; applications with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction At present, the new coronavirus disease 2019 (COVID-19) has unpredictably spread Copyright: © 2022 by the authors. across the globe and severely impacts human lives, research activities, economies, and Licensee MDPI, Basel, Switzerland. industries across the globe. Because of its severe impacts and infectious behavior, manage- This article is an open access article ment and healthcare have become crucial issues despite innovative treatment, vaccines, distributed under the terms and and medical facilities [1]. Several research initiatives have been carried out to handle this conditions of the Creative Commons disease through emerging technologies including the Internet of Medical Things (IoMT), Attribution (CC BY) license (https:// robots [2], artificial intelligence (AI) [3], and unmanned aerial vehicles (UAVs) [4]. Disrup- creativecommons.org/licenses/by/ tive wireless, automated and digital technologies such as AI, machine learning (ML), deep 4.0/). learning (DL), and deep neural networks (DNNs) have shown potential in combating the Micromachines 2022, 13, 1593. https://doi.org/10.3390/mi13101593 https://www.mdpi.com/journal/micromachines Micromachines 2022, 13, 1593 2 of 34 coronavirus [5,6]. Automated technologies such as UAVs and robots with the potential to carry heavy payloads have been greatly used to smartly control the spread of this virus by minimizing human contact in different environments. These technologies can assist in medical testing, treatment, vaccine delivery, and accurate diagnosis. Several robotic technologies have been deployed to assist medical staff in virus symp- tom detection, assist infected patients, and minimize the further spread of the virus [7]. These promising technologies are helpful in social and physical distancing and virus con- tainment. These technologies can be used in any challenging environment where human activity is dangerous [8], as this virus has made the public realm into a crucially hostile envi- ronment. Potential intersections between COVID-19 management and robotic technologies are based on the following [8]: Minimizing human-to-human contact: Robots are used to reduce human activity along with ensuring cost-effectiveness, reliability, and efficiency in performing different tasks such as social services, military operations, healthcare, and logistics. Robots can minimize human functions to control pandemics by reducing virus transmission, managing social distancing, controlling human contact, and operating autonomously. Managing, monitoring, and controlling mobility: Quarantine, lockdown, and control- ling human mobility play a major role in preventing the further spread of this disease. AI, ML, and robotic technologies can significantly monitor human mobility and avoid human gathering by territorial control at different checkpoints. Similarly, UAV technology has proven its stature to curb the risk of COVID-19 [9]. Sev- eral countries including China, USA, Japan, and Australia have been using this technology to combat this virus. For instance, China has been using more than 100 UAVs to perform surveillance tasks in various cities [10]. This approach has proved to be useful to avoid virus transmission and ensure social distancing. Several other countries are considering UAVs for vigilance, governance, delivering food and medical supplies, thermal scanning, monitoring, and sanitization. In the US, UAVs have been deployed to deliver medical sup- plies such as personal medical kits for COVID-19 to distant places [10]. In Australia, UAVs have been used to monitor patients with doubtful virus symptoms [11]. Sensors integrated into UAVs are used to monitor respiratory rate, heartbeat, body temperature, and other abnormalities. In addition, effective results in combating this virus can be achieved by integrating UAVs with other technologies such as thermal imaging, AI, ML, DL, and IoT. 1.1. Scope and Contributions The key aim of this review article is to assist is to educate readers who are interested in deploying UAV technology to combat COVID-19. Keeping this interest in mind, this article presents a brief overview of UAV technology and its potential to curb the risk of COVID-19 and reviews the state-of-the-art UAV technology in numerous applications, including spraying disinfection, transmitting messages through QR codes or loudspeakers, delivering medical supplies, surveillance, inspection, screening, and detection. This paper also examines practical approaches for combating COVID-19 through the integration of promising technologies including AI, ML, DL, IoT, and edge computing. Extensive assessments are conducted for several feasible solutions leveraging technologies including robotics, sensors, wearable devices, and virtual reality (VR). This study concludes with an analysis of the present problems, security issues, potential, and recommendations for future usage of these technologies for COVID-19 and similar diseases. 1.2. Organization of the Paper This paper is organized as follows: Section 2 is focused on the related research con- tributions to UAVs to combat COVID-19. Section 3 provides basic information about UAVs. Section 4 addresses different applications of UAVs to handle COVID-19. Section 5 is devoted to highlighting the role of different emerging technologies in minimizing the risk of COVID-19. Several open challenges, security issues, opportunities, and future recommendations are briefly explained in Section 6. Finally, Section 7 concludes the paper. Micromachines 2022, 13, 1593 3 of 34 2. Related Work A human viral illness called coronavirus disease 2019 (COVID-19) is associated with significant respiratory distress. Because of the effects of COVID-19 infection and its outbreak on human health, the World Health Organization (WHO) has designated it as a Public Health Emergency of International Concern (PHEIC) as of January 30, 2020 [12]. Given that infection spreads quickly across large populations, COVID-19 has been classified as a pandemic illness. The greatest way to prevent its transmission, according to WHO, is to take precautions as soon as possible. Healthcare organizations are primarily concentrating on implementing extensive testing and successfully treating sick people in order to combat the pandemic. Additionally, countries agree that enforcing lockdowns in infection-prone areas is the greatest strategy for reducing infection and bringing sustainability to the present pandemic crisis. Therefore, a specific framework for reshaping society is needed in order to execute the lockdown while still fulfilling the requirements of the healthcare system. In these conditions, smart technologies have had an influence on a number of areas, including healthcare, government, and business. The use of technologies such as telemedicine [13], artificial intelligence (AI) [14], robots [7,8], and drones [4] has increased significantly throughout the pandemic to bear the burden of frontline health system soldiers. Robotic and drone technology has been set up to help with monitoring, early infection screening, transportation of medical supplies, disinfection, and other processes. Robotics has also been extensively used to provide patients and healthcare professionals with medical advice. AI has been used in forecasting areas with infection concerns, prescribing medication, computing diagnosis findings, and other applications because of its capacity to operate independently, self-learn to improve with the addition of fresh data, and draw imperatives in short periods of time. Even the mobile phone network is seen as a crucial tool for combating the pandemic condition. Mobile phone technology is now widely used in cities and digitally connects people to bigger populations. Smartphone apps (Apps) have been used as the ideal model to interact with wider populations since mobile phone networks have been compared to the veins of cities. Numerous smartphone apps have been created to raise awareness, offer medical help, and most importantly, stop the COVID-19 virus from spreading by tracing contacts. Authorities are concentrating on the adoption of protective measures and quick testing techniques as we prepare to walk out and restore normalcy to everyday life. In this sense, the usage of face masks has shown to be successful in reducing the COVID-19 virus’s ability to propagate. Due to the unexpected increase in mask demand, new advancements in mask manufacturing technology have been made. A defensive line against COVID-19 has been established thanks to the collaboration of several industries, the use of technology, and competent governance. Understanding how these advancements are working together to tackle the pandemic crisis is crucial given the technology’s astounding contribution to the fight against COVID-19 infection. In Table 1, pandemic- and disaster-related UAV-based systems are summarized. Table 1. UAV-based system for pandemic or disaster analysis. Reference Year System Purpose [15] 2018 UAV-aided system for medical applications [16] 2018 UAV system for post-disaster operations [17] 2019 UAV-based system for healthcare use cases [18] 2019 UAV system to deliver medical drugs [19] 2020 UAV system for medical supply during disaster in Japan [9] 2021 UAV-based system to combat COVID-19 [20] 2021 UAV-based system to monitor social distancing [21] 2021 UAV-based system to supply medical resources during epidemics [22] 2022 UAV-based smart visual sensing for overcrowding [23] 2022 UAV-based isolation control proposal for COVID-19 Micromachines 2022, 13, 1593 4 of 34 The Crisis due to the COVID-19 Pandemic The marketing of many consumables has been disrupted, and several locations have seen the closure of stores selling non-emergency supplies. Large firms are being requested to provide masks, ventilators, kits, and other essential forms of equipment to assist in avoiding this sickness since there is a scarcity of medical supplies. Nevertheless, they are dealing with a major logistical problem [24]. Among the worst-affected industries is the tourism and travel sector. Here, the majority of domestic and international travel has been halted, and travelers have postponed their plans for important events throughout the globe. Many businesses are closing, and in certain countries, television shows are even being produced. There have been several postponements of international athletic events [25]. The governance frameworks are under scrutiny. People are frustrated and outraged with the situation, especially with those who are in charge of it. They dispute the significant spending on arms and research support for the armament business, arguing that if these funds had been allocated to public health, a pandemic of this nature would not have happened. This rage can occasionally be directed against those who are actually responsible for the transmission of this disease. Additionally, we observe that catastrophe managers are the targets of people’s rage. People are prepared to do nothing and wish to avoid infection. The general public must stay away from crowded areas and public transportation. Sadly, social media spreads false information and misinformation, which demotivates individuals. Additionally, social events such as marriages, vacations, weddings, and other leisure activities are postponed or canceled [26,27]. Transportation and logistics are a major problem during this epidemic. 3. Unmanned Aerial Vehicles (UAVs) Unmanned aerial vehicles (UAVs), also referred to as drones, have received momen- tous attention in various domains of civil and military operations because of their high mobility, enhanced stability, low cost, and high endurance in multiple tasks. The use cases of UAVs are extensively growing due to the integration of various emerging technologies such as 5G/B5G, artificial intelligence, Internet of Things (IoT), and mobile edge computing. UAVs are being used in a wide variety of applications such as logistics, forest monitoring, construction, freight transportation, communication, healthcare, post-disaster operation, search and rescue, remote sensing, agriculture precision, power-line inspection, traffic surveillance, and object detection and tracking [28–30]. In general, UAVs are controlled aerial vehicles which do not carry any human pilot. UAVs can be operated autonomously and remotely through microprocessors, sensors, and other equipment [31]. UAVs make use of communication links to connect with satellites or ground control stations (GCSs) such as smart phones or laptops. To perform remote operation, a human operator is required to control the UAV using a remote control. The use of UAVs is highly encouraged in scenarios where human intervention is strictly limited or hazardous. Due to the overwhelming interest in drones [32–34], several UAVs of varying sizes and forms (shown in Figure 1) have been developed to perform a variety of tasks. The categories of UAVs—single-rotor, multi-rotor, fixed-wing, and hybrid UAVs—have been used in the pandemic and other industrial applications. Each UAV has advantages and disadvantages that help us choose the one that is best suited for the application as shown in Table 2. Different characteristics of these UAVs are also summarized in Table 3. Micromachines 2022, 13, 1593 5 of 35 control. The use of UAVs is highly encouraged in scenarios where human intervention is strictly limited or hazardous. Due to the overwhelming interest in drones [32–34], several UAVs of varying sizes and forms (shown in Figure 1) have been developed to perform a variety of tasks. The categories of UAVs—single-rotor, multi-rotor, fixed-wing, and hybrid UAVs—have been used in the pandemic and other industrial applications. Each UAV has advantages and disadvantages that help us choose the one that is best suited for the application as shown in Table 2. Different characteristics of these UAVs are also summarized in Table 3. Table 2. Applications, advantages, and disadvantages of different types of UAVs. UAV Type USD Price Applications Advantages Drawbacks Structural inspection, Large area coverage, long Launching, Fixed wing USD 20,000–150,000 area survey endurance, high speed landing, high price Rotary wing (helicopter) USD 20,000–150,000 Supply drops, inspection Hovering, large payload High price Photography, Rotary wing USD 3,000–50,000 filmography, Hovering, availability, low price Short flight time, (multicopter) inspection small payload Table 3. Characteristics of different UAVs. Characteristics Fixed Wing Rotary Wing Hybrid Energy efficiency High Low High Flight system Complicated Simple Complicated Landing Conventional Vertical Vertical Autonomy No Yes Yes Hovering No Yes Yes Power supply Battery, fuel Battery Battery, fuel Endurance 60–3000 m 6–180 m 180–480 m Micromachines 2022, 13, 1593 Payload 1000 kg 50 kg 10 k5go f 34 Weight 0.1–400,000 kg 0.01–100 kg 1.5–65 kg FFigiguurere1 .1D. Diffiefrfenret ntytp teyspoefsU oAf VU:A(aV) r: o(taa)r yrowtainryg ,w(bi)nfigx, e(db)w fiinxge,d( cw) fiinxge,d (-cw) ifnigxehdy-bwriidn[g3 5h]y. brid [35]. T4a.b Alep2.pAlipcpalticioatniosn so,fa dUvAanVtasg edsu, arnidngdi sCaOdvVanItDag-e1s9o f different types of UAVs. UAV Type USD Price Applications Advantages Drawbacks Fixed wing USD 20,000–150,000 Structural inspection, Large area coverage, long Launching, landing,area survey endurance, high speed high price Rotary wing (helicopter) USD 20,000–150,000 Supply drops, inspection Hovering, large payload High price Rotary wing (multicopter) USD 3,000–50,000 Photography, Hovering, availability, Short flight time, filmography, inspection low price small payload Table 3. Characteristics of different UAVs. Characteristics Fixed Wing Rotary Wing Hybrid Energy efficiency High Low High Flight system Complicated Simple Complicated Landing Conventional Vertical Vertical Autonomy No Yes Yes Hovering No Yes Yes Power supply Battery, fuel Battery Battery, fuel Endurance 60–3000 m 6–180 m 180–480 m Payload 1000 kg 50 kg 10 kg Weight 0.1–400,000 kg 0.01–100 kg 1.5–65 kg 4. Applications of UAVs during COVID-19 UAVs have shown great potential in smart cities worldwide. Smart cities have smart healthcare systems based on telemetry, implantable medical equipment, and medical drones to quickly deliver first-aid supplies. UAVs have proven their stature to tackle the COVID-19 pandemic in different countries. However, it is worth noting that the leading organization tackling COVID-19 is the national EMS institution along with several parties such as EMS personnel, nurses, and medical doctors. In addition, several policymakers are considering different preventive measures to fight against COVID-19 including wearing surgical masks, avoiding facial touching, regular handwashing, city lockdown, high-risk area avoidance, social gathering avoidance, and implementing health codes. Policymakers should consider the balance between the economy and public safety before introducing any new measures. Currently, UAVs are being used to perform various tasks (as shown in Figure 2) to prevent COVID-19 such as: Micromachines 2022, 13, 1593 6 of 35 UAVs have shown great potential in smart cities worldwide. Smart cities have smart healthcare systems based on telemetry, implantable medical equipment, and medical drones to quickly deliver first-aid supplies. UAVs have proven their stature to tackle the COVID-19 pandemic in different countries. However, it is worth noting that the leading organization tackling COVID-19 is the national EMS institution along with several parties such as EMS personnel, nurses, and medical doctors. In addition, several policymakers are considering different preventive measures to fight against COVID-19 including wearing surgical masks, avoiding facial touching, regular handwashing, city lockdown, high-risk area avoidance, social gathering avoidance, and implementing Micromachines 2022, 13, 1593 health codes. Policymakers should consider the balance between the econom6yo fa3n4d public safety before introducing any new measures. Currently, UAVs are being used to perform various tasks (as shown in Figure 2) to prevent COVID-19 such as: •• TrTarnasnpsoprotrot fovf avcaccicnienseas nadndm medeidciaclakl iktsit;s; •• PPuublbilcica nannnououncnecmemenetnst;s; •• CCrorwowdds usurvrevielliallnacnec;e; •• SpSpraryaiyninggd disiisnifnefcetcitoino;n; •• MMasassss csrcereeneinnign;g; •• CCrorwownna eareirailaml monointiotroirnign;g; •• DDeleilvievreirnigngv avcacicniensesa nadndo tohtehrerm medeidciaclasl uspuplpielise.s. Figure 2. Role of UAVs in COVID-19 pandemic. Figure 2. Role of UAVs in COVID-19 pandemic. 4.1. Surveillance and Inspection 4.1. Surveillance and Inspection TThheem mosotscto cmommmononu suasgaegoef of drones is for area monitoring or aerial surveillance, both roinu trionuetoinpee roapteiornatsioannsd adnudr idnugrsin dgr osnpeesc is for area monitoringpecial oipael roaptieornatsiotonsb atott lbeaCttOle o Cr aOeVriIaDl surveillance, boVID-19. -A19re. aAmreoan mitoonriint tohriing onrg aeorri aalesruiarvl esiullravneciellwanacsee mwpaslo eymedpilnoyeevder yinc oeuvnertryy cwohuenrteryd rwonheeraep pdlricoanteio naps pwliecraetrioecnosr dweedr.e Wrehceonrdviesdu.a Wllyhwena tvchisiunagllayc weratatcinhirnegg iao nc,esretavienr arlefgaicotno,r ssemvuesrtalb efatcatkoerns imntuostc obnes itdaekreant ioinnt,o inccolnusdiidnegrathtieona,r eian’cslusidzien,gto tphoeg ararepah’isc aslizfeea, ttuorpeosg, srpapachiiocuals nfeeasstu, breusi,l ts-uppacriaotuiosn, leoscsa, tbiounilst-oufp praarttiiocu, lloacraitmiopnosr otafn pcaertwiciuthlainr itmhepaorretaan, cthe ewaipthpirno athceh aproeian, ttshoef atphperaoraecah, apnodintths eodf uthrea taiorena, oaf nthde tohbes edruvraattiioonn. Poof itnhtem oobnsietrovraintigonm. aPyobinet amdeoqnuitaotreining smituaayt iboen saidnevqoulavtien gins msiatullaetrioonrs triannvsoplvarineng tsompaelnlesr poarc etrsa; ninspsairtuenatt ioonpsenin svpoalvceins;g inla rsgiteuraatiroenass , itnhveodlveipnlgo ylamrgeenrt aorfemasa, ntyhe ddroenpelosyamrraenngt eodf minaanyn edtrwoonreks iasrnraenedgeedd ;iann ad nientwsitourakt iios nnseiendveodl;v ainngd pina rsti-ttuimateioonbss einrvvaotlivoinn,g itpisarets-tsiemntei aolbtsoeprvaatrtoiolnw, iitth iso ensesednrotinael t[o4 ]p. aDtrrooln wesitcho uonlde edvreonnek e[4ep]. aDnroenyeeso cnotuhlidn gevsewnh keereep thaenr eeyise noont mthuincghsh wumhearne itnhveorelv eism neontt .mTuhceh ehffuomrtaann dintvimolveenmeeednte.d Tthoek eefefporat naneyde toimne thneeseedpelda cteos kaereept oaon geryeea to. nT htheeisnev paslaiocneso afrper tiovoa tgerreiagth. tTshise oinnveaosfiothne omf pairnivoabtes traicglhestst oist ohnee drone-based monitoring of individuals. However, by releasing recommendations on public interests, governments are attempting to uphold these rights. Since 2019, COVID-19 has been mysterious; it has impacted a number of health professionals, including doctors, nurses, cleaning staff, and security personnel. Security staff may monitor unwanted movements and follow activity in hotspot regions without physically visiting them by using drones. They have the ability to take pictures of alleged violators of the occasional preventive instructions provided by governments. There are several benefits to performing this type of surveillance using drones, including avoiding COVID-19 infection among security personnel; saving gasoline and other resources that are often used for physical surveillance; and anytime, anyplace surveillance. Police officers are reluctant to visit hotspots in the absence of drones, but this is a necessary aspect of their job and they must. Since it is the responsibility of the government to protect security personnel Micromachines 2022, 13, 1593 7 of 35 of the main obstacles to the drone-based monitoring of individuals. However, by releasing recommendations on public interests, governments are attempting to uphold these rights. Since 2019, COVID-19 has been mysterious; it has impacted a number of health professionals, including doctors, nurses, cleaning staff, and security personnel. Security staff may monitor unwanted movements and follow activity in hotspot regions without physically visiting them by using drones. They have the ability to take pictures of alleged violators of the occasional preventive instructions provided by governments. There are several benefits to performing this type of surveillance using drones, including avoiding COVID-19 infection among security personnel; saving gasoline and other resources that are often used for physical surveillance; and anytime, anyplace surveillance. Police officers are reluctant to visit hotspots in the absence of drones, but this is a necessary aspect of their job and they must. Since it is the responsibility of the government to protect security personnel from COVID-19, most of them are giving their monitoring departments’ access to drones. In order to include multiple flying ad hoc network (FANET) devices in the COVID-19 task teams, government authorities are modifying their rules. Together with monitoring human mobility [36], security personnel can provide people advice on how to avoid crowding, minimize one-on-one interaction, emphasize the value of social distance, etc. To perform these tasks, the authors of [37] used a drone swarm in a specific area. A camera and sensor were mounted on the ground Micromachines 2022, 13, 1593 to capture videos and a real-time view of that area. The medical staff operating7 otfh3r4ough GCS can trace infected people through thermal cameras. Cameras can also monitor social distancing, as shown in Figure 3. Drones can gather information from the GCS and can deliver it to the public through voice or display screens. Table 4 summarizes drone frtoemchCnOolVogIDy- u19s,emd ofosrt osuf trhveemillaanreceg itvaisnkgs tihne dirimonitoringIn order to include multiple flying ad hoc netwoffrekre(FnAt NcoEuTn dtreipeasrtments’ access to drones.) devi.c es in the COVID-19 task teams, government authorities are modifying their rules. Together with monitoring human mToabbilliet y4. [D36ro],nsee tceuchrintyolopgeyrs uosnende flocr asnurpvreoilvlaindcee pdeuorpinlge tahdev CicOeVoInD-h1o9w patnodaevmoiicd. crowding, Reference Country minimize one-on-one interaction, emphaOsizpeerthateiovnal ue of social distance, etc. To perform [38] Kazakhstan these tTaosk psa, tthroel aauntdho mrsoonfit[3o7r ]ilulesegdala bdorrodneer sawctairvmitiiens atos psetocipfi cthaer esap.rAeacda mofe rCaOaVndIDse-1n9s or [39] France were mounted on theTgor opuantrdolt oovcearp atu crleosveidd eboesaachn dtoa mreoanl-ittiomr epuvbielwic of that area. The medical staff operating through GCS can trace infected people through thermal cameras. [40] Australia Cameras can also monitoTro smocoinalitdoirs staoncicainl gd,isatsanschionwg nati nthFei gbuearech3 . Drones can gather [41] India information fromToth eenGsuCrSe aloncdkcdaonwdne liimvepr lietmtoetnhteatpiuobnl ibcyt hmrooungithovrionigce pourbdliiscp lay screens. [42] Spain Table 4 summaTrioze ms donroitnoer tsetcrheneotsl ofgoyr aunseydonfoer igsunrovreinillga nCcOeVtaIsDk-s1i9n ldoicfkfedroenwtnc ountries. Figure 3. UAV swarm in COVID-19 pandemic [37]. Table 4. Drone technology used for surveillance during the COVID-19 pandemic. Reference Country Operation [38] Kazakhstan To patrol and monitor illegal border activities to stop thespread of COVID-19 [39] France To patrol over a closed beach to monitor public [40] Australia To monitor social distancing at the beach [41] India To ensure lockdown implementation by monitoring public [42] Spain To monitor streets for anyone ignoring COVID-19 lockdown 4.2. Broadcasting Messages Using Loudspeaker on Board In order to spread information, drones equipped with speakers, flags, QR codes, etc., are essential, especially during epidemics. Authorities can use these drones to some extent to deliver crucial messages, particularly in areas where communication links are down. These drones can prove to be one of the most effective communication tools during lockdowns. Municipal corporations can use these drones with sky speakers to reach out to residents in their areas with important messages (such as updates about a lockdown extension, information about which stores are allowed and which are not, health- related communications, implementation of social distancing, and other precautionary guidelines). Due to the fact that most people rely on TV for their communications, especially in rural regions, this sort of communication can break down barriers between municipal Micromachines 2022, 13, 1593 8 of 34 corporations and the populace. Figure 4 shows how a UAV with a speaker works to provide alerts to the public and keep individuals safe at home. Drones with speakers are now being used by numerous nations, especially the US, Spain, Britain, and China to communicate with those affected by the COVID-19 epidemic. The usage of a loudspeaker must consider loudspeaker efficiency and interference, the quality of voice transmission, the pitch and speech segmentation employed, and the space between the flying location of the drone and the individual or group to be briefed. However, entering a public zone belonging to a person is already evidently an issue of flight safety; keeping a minimum safe spacing of 5 m is justifiable. At longer drone-to-human distances, it may be considered that the target’s information receipt is instructive; at relatively short distances, it is a warning, and in the proximity of the open zone, it is already threatening. The action of a drone that maneuvers unexpectedly or dramatically in close proximity and communicates instantly, potentially at an excessive level, may be meant as a warning but may be perceived as menacing. These speaker-capable drones can aid with crucial communications, alerts during hazardous tasks, or rescue missions in addition to just chatting. Such drones can show themselves to be effective weapons in vast, crowded, and challenging locations while dealing with calamities such as floods, earthquakes, and fires. Despite these benefits, during emergencies and epidemics, they could keep authorities away from close contacts, preventing any likelihood of infection from risky actions and ensuring that the authorities can confidently perform their crucial jobs. Table 5 summarizes drone technology used to broadcast messages in different countries to implement COVID-19 precautionary measures. Table 5. Drone technology used to broadcast messages for COVID-19. Reference Country Purpose [43] USA To broadcast a warning to suspected people to follow social distancing rules [44] Spain Police used drones to yell at the public for being outside and ignoring the lockdown [45] China Drone mounted with a speaker to inform the public to wear a mask [46] Malaysia To give announcements and alerts to the public to curb COVID-19 spread [47] Hungary To inform the public to stay at home due to COVID-19 Micromach[i4n8e]s 2022, 13, 1593 USA Anti-COVID-19 volunteer drone to ensure social distancing of 6 feet 9 of 35 [49] Rwanda To broadcast a crucial message about COVID-19 FFiigguurree4 4.. BBrrooaaddccaassttiinngga am meessssaaggeeb byyu ussiningga al olouuddssppeeaakkeerr[ 5[500].]. 44.3.3.. BBrrooaaddccaassttiinngg MMeessssaaggeessb byyT ToowwininggQ QRRC CooddeeF Flalagga annddI lIllulummininaatitoionn TThheerree aarree vvaarriioouuss eexxaammpplleess ooff uussiinngg aa ddrroonnee ttooww aa QQRR ccooddee bbaannnneerr iinn ppllaaccee ooff aa lloouuddssppeeaakkeerrt otof afcaicliitlaittaetceo cmommumnuicnaitciaotnio. nY.o uYocaun cuasne uasQe Ra cQodRe cfloadgei nflaadg diinti oanddtoitoiornr attoh eorr trhaatnhear lothuadns peaa kloeru.dWspheialkeeQr.R Wcohdieles aQpRpe acrodtoesb eaapp“setaart ict”o vberes ioa n“osftactoicm” mvuenriscioatni ono,f lcooumdsmpueankicearstioapnp, elaorutdosbpeeaak“edrsy naapmpieca”rv etros iobne sian c“edtyhneyammiacy” bvroerasdiocans ts“ilnivcee ” tmheeyss amgeasy tbortohaedwcahsto le“lpivueb”l icm. eIsnsathgeesfi rtsot stcheen awrihoo, lteh eptuabrgliect.e dInp othpeu lafitrisotn sicsebneairnigo, adthder etsasregdetbeyd tphoepdurloantieono pies rbaetoinrgo radmdirsessiosendc obmy mthaen ddreornwe hoopecraantorre sopro mndistsoiotnh ecosmitumaatinodnewr withhob rcieafn respond to the situation with brief messages right away in a dynamic environment. In the second scenario, the environment is more or less static and the operator or commander must deliver more complex but standard messages. In the latter scenario, it goes without saying that the targeted demographic will need to utilize their cell phones to obtain the required information online. In many locations, such as crossings, mall parking lots, congested roads, and large-scale sporting events where the border or gate crossing procedure or the staying situation might vary daily, using a QR code can be useful. On the other side, Chinese authorities are utilizing UAVs to scan drivers entering Shenzhen city by placing overhead QR codes [51]. Figure 5 depicts a motorist who is returning to Shenzhen registering by scanning the QR code. These UAVs were flown close to highway exits or toll booths to encourage approaching vehicles to register so that authorities could follow their movements around the city. However, the drone must be in close proximity, and this application can also cause challenges such as delays and traffic congestion. Additionally, it must be smartly controlled and it should be very close in order to avoid any physical or economic hazards. Figure 5. Broadcasting a message through a QR code [51]. In 2020, 300 drones were used to illuminate the sky to honor frontline heroes, medical workers, and patients in the Netherlands [52]. In Seoul, South Korea, hundreds of drones performed a show displaying messages to support the country in the fight against COVID-19. The show started with showing precautionary steps and ended with Micromachines 2022, 13, 1593 9 of 35 Figure 4. Broadcasting a message by using a loudspeaker [50]. 4.3. Broadcasting Messages by Towing QR Code Flag and Illumination There are various examples of using a drone tow a QR code banner in place of a loudspeaker to facilitate communication. You can use a QR code flag in addition to or rather than a loudspeaker. While QR codes appear to be a “static” version of Micromachines 2022, 13, 1593 communication, loudspeakers appear to be a “dynamic” version since they9 omf 3a4y broadcast “live” messages to the whole public. In the first scenario, the targeted population is being addressed by the drone operator or mission commander who can mresspaogneds rtiog thhtea switauyaitnioan dwyintham bricieefn mviersosnamgeesn rti.gIhntt ahwe saeyc ionn ad dscyennaamriioc, ethneviernovnimroennmt.e Int tihse msoerceonordl esscsenstaartiioc,a tnhde tehnevoirpoenramtoernot risc ommomrea nodr elersms ustsattdice laivnedr tmhoe roepceormatpolre xobr uctosmtamndanardder mmesussatg deesl.ivIenr tmheorlea tctoermspcleenxa bruiot, sittagnodeasrdw mithesosuatgseas.y Iin gthteh alattthere stcaerngaertieod, idt egmoeosg wraipt hoicut wsiallyninege dthtaot uthtieli ztaertgheetierdc delelmpohgornaepshtioc owbitlal inetehde troe quutiilriezde tihnefoirr mceallt ipohnoonnelsin teo. oIbntamina ntyhe lorceaqtuioirnesd, siuncfhoramsactriosns ionnglsi,nme. aIlnl p marakninyg loloctast,ioconns,g esustcehd aros acdrso,sasnindglsa, rmgea-lslc aplaerskpinogrt ilnogts, evcoengtseswtehde rerotahdesb, oarndde rlaorggea-stceaclreo sspionrgtipnrgo ceevdeunrtes owrhtheree stahyei nbgorsditeura toior ngmateig hcrtovsasrinyg dpairloyc, eudsuinrge aorQ tRhec sotdaeyicnagn sbiteuuastieofun lm. ight vary daily, using a QR code can be useful. OOnnt htheeo tohtherers isdide,e,C Chhinineseesea uauththoorirtiiteiessa arereu utitliilzizininggU UAAVVs st otos csacnand drirvievresrse netnetreirningg ShShenenzhzhenenc ictiytyb ybyp plalcaicninggo voevrehrheaedadQ QRRc ocdodeses[ 5[15]1.].F Figiguurere5 5d depepicitcstsa am moototorirsitstw whhooi sis rerteuturnrnininggt otoS hShenenzhzhenenr ergeigsitsetreirninggb ybys csacnannininggt htheeQ QRRc ocdoed.e.T ThheesseeU UAAVVssw wereerefl folwownn clcoloseset otoh higighhwwayaye xeixtistso or rt otollllb booooththsst otoe nencocouurargageea pappproroacahchininggv vehehicilcelsest otor ergeigsitsetrers osot hthatat auauththoroirtiiteisesc ocouuldldf ofolllolowwt htheierirm moovvememenentstsa arorouunnddt htheec ictiyt.yH. Hoowwevevere,rt,h tehed drornoenem muustsbt ebein in clcolosesep proroxixmimitiyt,ya, nadndth tihsisa papplpicliactaitoinonc acnana laslosoc acuausesec hcahlallelnenggesess usuchcha sasd delealyayssa nanddt rtarfafiffcic cocnongegsetsiotino.nA. Adddidtiiotinoanllayl,lyit, mit umstubset sbme asrmtlyarctolyn tcroolnletrdolalnedd iatnsdho iut lsdhboeuvlde rbyec lvoesreyi ncloorsdee rin tooradveori dtoa anvyopidh yasniyca pl hoyr seiccoanl oomr eiccohnaozmaridc sh. azards. FiFgiugruere5 .5B. Broraodadcacsatsitnignga am mesessasgaegeth trhoruoguhgha aQ QRRc ocdoede[5 [15]1. ]. InIn2 022002,03, 0300d0r odnroesnews ewreeures eudsteodi ltlou mililnuamteintahtee stkhye toskhyo ntoo rhforonnotrl infreonhtelrionees ,hmereode-s, icmalewdiocarkl ewrso,raknerds,p aantide nptastiienntths einN tehteh eNrelathnedrsla[n52d]s. [I5n2]S. eIonu Sl,eoSuolu, tShoKutohr eKao, rheuan, dhurenddsreodfs Micromachines 2022, 13, 1593 o 10 of 35 drof ndersopnersf opremrfeodrma eshdo aw sdhioswpl adyiisnpglamyeinsgsa gmeesstsoagsuesp pto rstuthpepocortu nthtrey cionutnhteryfi ginh ttahgea ifnigsht t CaOgVaiInDs-t1 C9.OTVhIeDs-h1o9w. Tshtea rsthedoww isttharstheodw winitgh psrheocwauintigo nparreycasutetpiosnaanryd setnedpesd awndit hensdheodw winigth gsrhaotiwtuindge gtoramtiteuddiec atol pmeerdsiocnaln pelerasnodnntehle apndu bthliec pfourbltihc efiorr ctohlelierc ctoivlleecetfifvoer etsffaogrtasi angsatiCnsOt VID-19, aCs OshVoIwD-n19in, aFs isghuorwen6 i.n Figure 6. Figure 6. Broadcasting messages through the illumination of drone swarms in South Korea [53]. Figure 6. Broadcasting messages through the illumination of drone swarms in South Korea [53]. 4.4. Disinfecting Surfaces and Common Areas As COVID-19 instances increase at an enormous speed, it is clear that most cases are brought on by surface contact with objects found in public spaces, such as chairs, tables, railings, door handles, elevator buttons, public transportation, and shopping centers. Depending on the environmental conditions, the COVID-19 virus can remain present on various surfaces for a few hours to many days. The first step in preventing secondary transmission is to sterilize these surfaces and locations. In general, air disinfection and surface disinfection are the two most popular methods to improve disinfection. In comparison to air decontamination, surface decontamination was more successful in managing the COVID-19 epidemic. Air disinfections, on the other hand, can offer psychological relief and emotional reassurance that the authorities are making an effort to handle the crisis, and they can also keep rats away. Among the several methods for air sanitization, drone spraying has proven to be beneficial. According to their power and tank capacity, spraying drones often come in several variants. A generalized UAV spraying concept with a small tank is shown in Figure 7. While sanitization employees may be exposed to viral diseases while performing their vital jobs, these UAVs are simple to operate and mobilize and prevent the operator from becoming ill. On the other hand, although having a number of benefits, air disinfection has not been shown to be a reliable way to stop the transmission of viruses. Alcohol-based and other chemicals should not be sprayed excessively on people, cars, or the environment since they are extremely dangerous. Drones can be used to spray disinfectant into public areas when no one is there, such as at the end of the day or on vacations. Sprays based on agriculture should never be used since they are more dangerous than air disinfectants. Recently, several studies have been reported on employing drones to spray disinfecting solutions against novel coronaviruses [54]. It is important to note further scientific studies that are creating strategies to handle the COVID-19 epidemic more expertly. The study by Kumar et al. [9] focused on interconnected drone-based systems, such as the potential use of drones for disinfectant spraying. In this trial, a 2 km radius was sprayed in 10 min; however, there was no information about the circumstances. Alsamhi et al. [55] provided a framework and suggested a mechanism for employing several drones in decentralized ways. The use of drones to spray disinfectant was also addressed in this study, although no specifics about the practice were provided. In [56], Micromachines 2022, 13, 1593 10 of 34 4.4. Disinfecting Surfaces and Common Areas As COVID-19 instances increase at an enormous speed, it is clear that most cases are brought on by surface contact with objects found in public spaces, such as chairs, tables, railings, door handles, elevator buttons, public transportation, and shopping centers. Depending on the environmental conditions, the COVID-19 virus can remain present on various surfaces for a few hours to many days. The first step in preventing secondary trans- mission is to sterilize these surfaces and locations. In general, air disinfection and surface disinfection are the two most popular methods to improve disinfection. In comparison to air decontamination, surface decontamination was more successful in managing the COVID-19 epidemic. Air disinfections, on the other hand, can offer psychological relief and emotional reassurance that the authorities are making an effort to handle the crisis, and they can also keep rats away. Among the several methods for air sanitization, drone spraying has proven to be beneficial. According to their power and tank capacity, spraying drones often come in several variants. A generalized UAV spraying concept with a small tank is shown in Figure 7. While sanitization employees may be exposed to viral diseases while performing their vital jobs, these UAVs are simple to operate and mobilize and prevent the operator from becoming ill. On the other hand, although having a number of benefits, air disinfection has not been shown to be a reliable way to stop the transmission of viruses. Alcohol-based and other chemicals should not be sprayed excessively on people, cars, or the environment since they are extremely dangerous. Drones can be used to spray disinfectant into public areas when no one is there, such as at the end of the day or on vacations. Sprays based on agriculture should never be used since they are more dangerous than air disinfectants. Recently, several studies have been reported on employing drones to spray disinfecting solutions against novel coronaviruses [54]. It is important to note further scientific studies that are creating strategies to handle the COVID-19 epidemic more expertly. The study by Kumar et al. [9] focused on interconnected drone-based systems, such as the potential use of drones for disinfectant spraying. In this trial, a 2 km radius was sprayed in 10 min; however, there was no information about the circumstances. Alsamhi et al. [55] provided a framework and suggested a mechanism for employing several drones in decentralized ways. The use of drones to spray disinfectant was also addressed in this study, although no specifics about the practice were provided. In [56], the authors used UAVs to perform disinfection tasks in some areas. They focused on the evaluation of static and dynamic behavior, as well as the impact of spraying flow, mission speed, and flying height. The recommended feasible height was 3 m for good performance. Table 6 summarizes drone technology used to spray disinfectants in some countries against COVID-19. Table 6. Drone technology used to spray disinfectants for COVID-19. Reference Country Purpose [57] China To spray disinfectants to stop the spread of COVID-19 [58] India To improve the efficiency and speed of the sterilization operationsin public places [59] Spain Spain’s military used agriculture drones to spray disinfectants tostop the spread of COVID-19 [60] USA Drone to spray disinfectants in large places [61] UAE Dubai Municipality conducts a massive sterilization drive againstCOVID-19 through drones Micromachines 2022, 13, 1593 11 of 35 the authors used UAVs to perform disinfection tasks in some areas. They focused on the evaluation of static and dynamic behavior, as well as the impact of spraying flow, mission speed, and flying height. The recommended feasible height was 3 m for good performance. Table 6 summarizes drone technology used to spray disinfectants in some countries against COVID-19. Table 6. Drone technology used to spray disinfectants for COVID-19. Reference Country Purpose [57] China To spray disinfectants to stop the spread of COVID-19 [58] India To improve the efficiency and speed of the sterilization operations in public places Spain’s military used agriculture drones to spray disinfectants to stop the spread of [59] Spain COVID-19 Microma[c6h0in]e s 2022, 13,U15S9A3 Drone to spray disinfectants in large places 11 of 34 [61] UAE Dubai Municipality conducts a massive sterilization drive against COVID-19 through drones FFiigguurree 77.. DDrroonnee uusseedd ffoorr sspprraayyiinngg ddiissiinnffeeccttaannttss [[6622]].. 44..55.. SSuuppppoorrttiinngg MMeeddiiccaall SSeerrvviicceess BByy uussiinngg ddrroonneess,, ddeelliivveerryy sseerrvviicceess ffoorr ffoooodd,, ppaarrcceellss,, mmeeddiiccaall ssuupppplliieess [[6633]],, eettcc..,, mmaayy bbeeccoommee qquuiicckkeerr aanndd mmoorree ssuussttaaiinnaabbllee aatt aa lloowweerr ccoosstt tthhaann mmoorree ccoonnvveennttiioonnaall ddeelliivveerryy mmeetthhooddss ssuucchh aass vvaannss aanndd vveehhiicclleess [[6644]].. MMaannuuaall ddeelliivveerryy mmaayy bbee rreeppllaacceedd bbyy tthhee ddeelliivveerryy mmeetthhoodd’’ss ttrraannssffoorrmmaattiioonn.. IInn oordrdeerrt otoin cinrecaresaesde eldiveleirvyereyf feecftfievcetinveesnseasns danqdu alqiutyaloitfys eorf- vseicrev,icues,i nugsidnrgo nderocnoen tcroonltmroal ymmaayk me aitkpeo ist spibolsestibolae ctcoo macpcolimshpaliustho mauattoicm, autnicm, aunnnmeadn, naendd, iannfdor minafotiromna-btiaosne-dbadsiestdr ibduisttiroinb.uTtihoins. wTihllisc lwosiell thcleosgea pthteh agtaepx itshtsatb eetxwisetes nboertwdeeresna nodrdtehres caanpda cthitey cfaopradceitlyiv feorry dseelrivviecreys .services. HHeeaalltthhccaarreep prroofefesssisoionnaalslsw wouoludldb eboen oenoef othf ethpero pmroinmenintesntat ksethakoeldheorlsdienrsm iann magainngagainndg parnodv ipdrinovgitdhiengse trhveic essertvoitchees gtoen tehrea lgpeunbelricala npdubalriec aatntdh eafroer eaftr otnhteo ffomreofdroenrnt aocf cempotadnecren oafccteecphtannocloeg oyf. Htecehalntholcoagrey.p Hroefaelstshiocanrael sphraovfeesasifoanvaolrsa hbalevev iae wfarveograarbdlien vgiethwe ruesgeaorfddinrgo ntehse, aucsceo rodf idnrgontoesa, astcucodrydicnogn dtou cat esdtuidnyO csolnod, uNcoterdw iany O[6s5lo]., NItoirswmayo r[e65c]r.u Icti aisl tmhaonre ecvreurcitaol ttrhaanns pevoertr vtoa ctcrai nesspotortr vemacocitneerse gtoio rnesmiontea rqeugiicokn,ss ienc uar qeuwicaky,, seescpuercei awllayyi,n estpheecCiaOllVy IiDn -t1h9e aCgOe.VDIDro-n19es amgaey. bDeruonseeds tmo dayel ivbeer uCsOeVd IDto- 19devlaivcceirn eCsOtoViInDa-c1c9e ssviabclecilnoecsa titon sin, aaccccoersdsiibnlge tlocsaetvioenrsa,l arcecsoeradrcinhgs ttou dseievserdael vroetsedarctoh tshtuisditeosp dicev[6o6t,e6d7 ]t.o Dthuisr itnogpitch [e66e,p6i7d].e mDuicr,inagf ethwe setpuidieems iecx, aam feinwe dstucudsiteosm exearmviienweds cuosntcoemrneirn vgiedwrosn ceondceelirvneinryg odfrogneen edreallivgeoroyd osf ogrefnoeordal; tghoeosdess tuodr iefos,otdh;e rtehfeosre , sdtiuddnieost, etxhaemreinfoerep,u bdliidc anpoptr oevxalmoifnder opnuebdliecl ivaeprpyroinvagle noefr adlr[o6n8]e. Tdheelirveefroyr e,init igsenimerpaol rt[a6n8]t. toThinevrefsotirgea, teit hiosw imhepaolrthtacnatr etop roinfevsessitoignatles fheeolwa bhoeuatltuhscianrge dpronfessstiondaelsli vferelm aebdoiucat tuiosninsga nddrovnaecsc itnoe sddeulivrienrg mtheedCicOatVioInDs- 1a9npda vnadcecminiecs. Mdualrainygsi athise aCcOtivVeIlDy-i1n9v epsatingdaetimngict. hMe ualsaeyosfiad rios naecstitvoedlye liivnevremsteigdaitciantgio nthsea nudsev aocfc idnreos.nNese vtoe rtdheellievsesr, studies on the adoption of drones as a method of delivering vaccines and medications to rural regions by healthcare providers are scarce. In a recent study [69], the authors evaluated the feasibility of drones to deliver medical supplies considering multiple aspects such as payload integration, fleet development, and medical delivery simulations. In [70], the authors used drones for drug distribution considering an optimum way. They focused on finding an optimized trajectory for drones to deliver a drug. They proposed a hybrid model containing K-mean clustering and ant colony algorithms. They compared the performance of different algorithms and stated that the ant colony algorithm yields the closest to optimum results. Drones used to deliver medical and food supplies during COVID-19 are shown in Figure 8. Table 7 summarizes the drone technology for delivery services in different countries. Massachusetts Institute of Technology (MIT) and UNICEF are working in collaboration to determine the impact of the use of drones to deliver vaccines in remote communities of Nepal [71]. They also analyzed the cost and advantages of drone usage in two districts and developed a set of recommendations. To ensure the complete availability of vaccines, they identified the optimal drone system, delivery hubs, payload, and range, showing a Micromachines 2022, 13, 1593 12 of 35 medications and vaccines. Nevertheless, studies on the adoption of drones as a method of delivering vaccines and medications to rural regions by healthcare providers are scarce. In a recent study [69], the authors evaluated the feasibility of drones to deliver medical supplies considering multiple aspects such as payload integration, fleet development, and medical delivery simulations. In [70], the authors used drones for drug distribution considering an optimum way. They focused on finding an optimized trajectory for drones to deliver a drug. They proposed a hybrid model containing K-mean clustering and ant colony algorithms. They compared the performance of different algorithms and stated that the ant colony algorithm yields the closest to optimum results. Drones used to deliver medical and food supplies during COVID-19 are shown in Figure 8. Table 7 summarizes the drone technology for delivery services in different countries. Massachusetts Institute of Technology (MIT) and UNICEF are working in collaboration to determine the impact of the use of drones to deliver vaccines in remote communities of Nepal [71]. They also analyzed the cost and advantages of drone usage in two districts and developed a set of recommendations. To ensure the complete Micromachines 2022, 13, 1593 availability of vaccines, they identified the optimal drone system, delivery hubs, pa12yolof a34d, and range, showing a transportation cost saving of USD 0.10–0.12 compared to baseline. The analysis reveals that cost savings can be achieved only when capital costs, including triannfrsapsotrrtuactitounrec ocsotsst,a vdienlgivoefryU sStDar0t-.1u0p–,0 a.1n2dc eoqmupiparmedentot pbraosceulinreem. Tehnet, aanrael yssuibssriedviezaelds.t hTahtis coesctosnaovminigc sacnaanlybseisa schhoiewvse dano ninlytrwighueinngc ainpviteasltcmoestnst, cinacsleu adnindg inintrforadsutrcuecst ua rseucsotasitn, dabelliev perayth staahret-audp t,oa nindteegqruaitpe mdreonntep treocchunroemlogenyt i,natroe hseuablsthid sizuepdp.lyT hcihsaeincos.n Aom Dieclaonitately rseipsosrhto awnsd atnhe in2t0r2ig1u Wingorilndv eEsctomneonmt icca sFeoraunmd irnetvroeadluecde sthaast udsrtaoinnea btelechpnaothlogahye acadnt obein ate gcroastte-edffriocineent teocphtnioonlo gfyori ngtoovheeranlmthesnutsp pilfy cthheayin sh. aAveD: ellaorigttee-srceapleo rtdaenpdloythmee2n0t2, 1eWxpoernldsivEec ongoromuincd Ftorraunmsproervtaetailoend, athnadt adnro anffeotredcahbnleo ldorgoyncea vnebnedoarc [o7s2t]-.e Tffihcuiesn, tthoep itmiopnlefomregnotavteiornnm oef ndtrsoinfe thteecyhhnaovloeg: yla rcgaen- scdaelcerdeaespel ocyomste natn, de xpenehnasinvcee gvroauccnidnet raavnaspiloabrtialittiyo ni,na nad wanidaef forarndgaeb leof dcriorncuemvesntadnocre[s7 a2n].dT sheuttsi,ntghse iifm thpilse tmecehnntaotlioognyo ifs derxotennestievcehlyn oulsoegdy tcoa onvdeerccoremasee thcoes ctaapnidtal encohsatnsc eovfa ccsiynseteamva iladbeipliltoyyimn eanwt idaenrdan gme aoifnctiercnuamncset.a ncTehsea ndmsaejottri ngdsriifvtehriss tetcoh neonlosguyre isceoxstte-enfsfeivcetilvyeunseesds taoreo vdeirsctaonmcee tthoe bcea ptritaavlecloesdt,s poefospylset etmo bdee pvlaocycmineantet dan, danmda rionatedn aspnecee.d Tahnedm caojsotr odf rciovnervsentotioennasul rgerocuonstd-e vffeehcitcilveesn [e7s3s].a Trehedries taarnec seetvoerbael ctrhaavlleelnedge, sp tehoaptl emtuostb ebe vtaaccckinleadte tdo, eannsdurreo acdonstpaecetldesasn ddelciovsetryo fofc ohnevaeltnhtcioarnea lsugpropulineds tvherhoiucglehs d[7ro3]n.esT. hFeirrest,a rthee servegeruallactihoanllse nasgseosctihaatetdm wusitthb ethtaec uklseadgeto oefn tshuirse tceocnhtnaocltolegsys dareeli vneorty corfyhsteaall tchlecaarre. Tsuhpepreli easre th crs roocuiaglhystal im drpoclear.a ncets. FThse; ree. igrs.,t , tharepseoo eprleeg ufelealt iaofnrsaiads stohcaita ttehde wexittehntshieveu saapgpeliocfatthioins st eocfh nthoilso gteycharneonotcial impacts; e.g., people feel afraid that the extensive applicatiolongsy ofcathni sdtaemchangoel othgey claabnodr ammaargkeetth, easl atbhoer tmraanrskpeot,ratasttihoen toraf ntshpeo prtraotdiounctosf dthoeesp rnoodtu ncetsedd oaensy nmotannepeodwaenr.y Tmecahnnpiocawl eirs.suTeesc hinnciclualdiinssgu ethsei nnculumdbinerg otfh eprnoudmucbtesr, ovfenpdroodrsu, cptsa,yvloeandd,o arns,d pwayelaotahde,ra cnodndwietiaotnhse rhcaovned ai tgiorenast himavpeaactg orena tthiem dpraocnteo dnetlhiveedrryo innedduesltirvye. ry industry. (a) (b) FiFgiugruere8 .8D. Drornoensesu suesdedto tod edleivlievrer(a ()am) medeidciaclasl uspupplpielises[6 [36]3a] nadnd(b ()bf)o foododsu spupplpielise[s7 [47]4. ]. Table 7. Drone technology used for delivery services during the COVID-19 pandemic. Reference Country Purpose [75] UK To deliver COVID-19 tests, medicines, and personal protectiveequipment (PPE) to remote communities in the UK [76] Ghana Zipline and UPS are working together to deliver COVID-19vaccines to health centers in remote areas [77] USA A North Carolina health system used UPS Flight Forward drones todeliver COVID-19 vaccines [78] UK An NHS drone-based delivery service is used to carry personalprotective equipment, blood tests, and COVID-19 samples [79] Indonesia A group of drones has been used for contactless delivery of foodand medicine [80] Scotland UK-based drone specialist Skyports is providing drone service todeliver medical supplies and COVID-19 samples [81] China Shanghai firefighters have been using drones to deliver medicinesduring lockdown [82] USA Walmart has started a drone delivery service to collect kits in NorthLas Vegas and New York 4.6. COVID-19 Screening and Detection A person-to-person encounter is one of the most frequent methods of coronavirus infection. Therefore, it is critical to quickly identify both the infected and potentially Micromachines 2022, 13, 1593 13 of 35 Table 7. Drone technology used for delivery services during the COVID-19 pandemic. Reference Country Purpose [75] UK To deliver COVID-19 tests, medicines, and personal protective equipment (PPE) to remote communities in the UK Zipline and UPS are working together to deliver COVID-19 vaccines to health centers in [76] Ghana remote areas [77] USA A North Carolina health system used UPS Flight Forward drones to deliver COVID-19 vaccines [78] UK An NHS drone-based delivery service is used to carry personal protective equipment, blood tests, and COVID-19 samples [79] Indonesia A group of drones has been used for contactless delivery of food and medicine UK-based drone specialist Skyports is providing drone service to deliver medical supplies [80] Scotland and COVID-19 samples [81] China Shanghai firefighters have been using drones to deliver medicines during lockdown [82] USA Walmart has started a drone delivery service to collect kits in North Las Vegas and New York 4.6. COVID-19 Screening and Detection Micromachines 2022, 13, 1593 A person-to-person encounter is one of the most frequent methods of 1c3oorfo3n4avirus infection. Therefore, it is critical to quickly identify both the infected and potentially contaminated parties as a result of the interaction. Massive and quick screening in the scuornrtoaumnindaintegd apraeratsie iss atshae rpelsaunlt, wofitthhe liitnttleer taoc tnioon .huMmasasniv-teoa-hnudmquanic kcosncrteaecnt.i nIgn itnhitsh eregard, dsurorrnoeu ntedcihnngoalroegays ihsatsh echpalanng,ewdi tthheli tgtlaemtoe naondh uamidaend-t oin-h tuhme amnacsosn stcarcet.eInninthgi sofr eignadridv,iduals bdarosende toecnh nsyolmogpytohmass c[h8a3n]g. eTdhtehseeg damroenaensd haaivdeed ai ntethmepmeraastsusrcer eseenninsgoro fminoduinvitdeuda lsbased on symptoms [83]. These drones have a temperature sensor mounted to keep visitortso keep vaiwsiatyorfsro amwtahye irfrhoomus tehs.eiAr dhdoituiosensa.l lAy,dthdeiytiohnavaellya ,c athmeeyr ahfaovret aak icnagmimeraag efos ro ftathkeinsgu bijmecatges of tahned sauGbjPeSct[ 8a4n]dfo ar lGocPaSti n[8g4t]h feomr .lAocnaotivnegr vtiheewmo.f Adrno noevetercvhienwol oogfy dtorofinned tiencfhenctoeldogaynd to find ihnefaelcthteydp aenodpl eheinalttheys ptreeoetpslcea in btehese setnreinetFs icgaunre b9e. seen in Figure 9. Micromachines 2022, 13, 1593 14 of 35 These test results would be accessible to remote medical workers via cloud services for additional analysis. The elimination of human touch due to process automation aids in the fight against COVID-19. This strategy can give older and physically challenged people access to crucial medical care and testing facilities. Covidrone is expected to help significantly in lowering COVID-19 curves, especially in remote areas where there are not many places to receive medical help. Table 8 summarizes the drone technology for the detection of COVID-19 in different countries. To handle the COVID-19 outbreak, smart healthcare will be developed with the use of detection technologies. Drones, the Internet of Things (IoT), fifth-generation cellular networks, and a DNN model are some of these technologies. Drones have historically been employed in defense-related tasks, such as remotely operating aerial missile FFliaigguuunrrceeh 99e..r Ds.rr oHonnoeewt eteechvchneonr,lo oilgnoy gryuec suedsnetfd oy rfeotahrre sthd, ede trdeocenttieoecsnt ihoofanivn oeff eb cientefdencp tueetodipl iplzeee[od3p7 fl]oe. r[ 3a7 v].a riety of purposes, including monitoring climate change, taking pictures and videos, and delivering blood. ReseIIanrc [h[11e]],r,s t hthe[8ea5 ua] uthstuohgrosgresp srpto rpeoompsoepdsloeCydio nCvgio dvnrioudnmreo,enrwoeuh, siwc hhUiuAcshVe s ubsswieosam rbmeidosi mcateold sieacndasdlo rsreessnst osovcraosrl iloteouc tsc ollect mmCeOeddViiccIDaall- i1in9nf foiosrsrmumeaasti,t oiionnncfl rufordmoinmpg epsooepcolieaplll eiins olkilenadktieotodn, C tsoOy mVCpIODtoV-m1I9D .s-uA1r9vs. eeAinlls aosnercnmes, ooardn udml eso,adnauidtliiezs,ai ntaifo ednci.ts iFiononfre ction mmpeoorddfouurlleme,,a ttenescsteitn iangngkd ik tssiet,csau, wrai tiwyre, iltrehesesleycs oasmd cvmoomcuanmtiecu athntieioc nuastseiyo sontfe 6smyG,s,a tbenlmdoca,k aDcnhNdaiN na, m DanoNddN eslo afmrtweoiadnrecll-u daderefeid nienindc luded iCnoe Cvtwiodovrroikdnireno’gsn pe(aS’syD lpoNaa)dy. ,lRoasoabdsho, otasw s ansnhindo Fwdigrnou nrinee s1F 0wi.gSeuurrece h u1ds0reo.d nS eubscych o tuhdlerd oalnauentsdh ocinrosus tlordaf tle[a9gn]i cdat olil nym cshotornasietoenrg ically caChreOoasVseIanDn -ad1r9we ahasio ttafsonprdoct suw saatonitmd feoorrfsf ewcru hsmotoenmdeeicedarels d awtshsheisoitrasecrev iwcehseargea iint .wSmothe drone acquire the necessary medical data aft enreaeudtehden ttihceaitrin sgetr uvalhei rdct mbee duesse raag rieacqainul isreends.o nd d.i sSinmfea rT cr shoe tti nm nir g edical splthenaestfsooerrnsms ionnalg stho apesp udproports a number of aratnues .acSquubisreeq tuheen ntleCcOesVsIaDry-1 m9 aecdtiivcaitli edsa, tian calfutderin agu stahneintitziacation, thermal imaging, crowd monitoring, and conyt,roalD. TNhNe v-biaabseild model receives th ting the user and disinfecting th ity of such systems’ eospeemraetidoincsa lisd sattiall fionr dCoOuVbtI Db-e1c9auid ee nsetinfisciantgio napapndaraantualsy. sSisu. bBsaesqedueonntltyh,e are sDuNltsNo-fbhaisseodr hmeor dteeslt ,rtehcese they may e u ivsees these medical data for COVID-19 niedeedn teixfitceantsiiovne annedtw aonrakl yressiso.u Brcaesse dan odn ctohoer dreinsautlitosn o wf hhiesn r will be a vised to take additional medicine or take prevent tive measures. These test or her tiemstp, ltehmee unsteedr wini lrle blaet iavely broad lor ul s would be acc ssdibvleisteodr etom toackaeti oandsd, istiute medicalocwnha al sm ae vdiilorkers claingee. oIrn taa kreec penret vsetundtayt i[v86e] ,m theae sures. aanuatlhyosriss . foTchueseedli moinn adtrioonneo-sfwhaurmma ntetcohuncohlodguye ttoo lpimroict vtihaec lsopurdeasde rovfi ces fess automation CaiOdV orIDad-1ditionals in th9e. fiTghheyt apgraoipnostseCdO aV IsDys-1te9m. T ahrischsittreactteugrye ctahnagt icvaeno ilddeerntainfyd tphhey isnicfaelcltyedch caallseensg beyd pesetoimplaetainccge sthsetoir cbriuocmiaeldmiceadl icaplacraarmeeatnerds.t esTtihnegyf acciolintiseisd.eCreodv idsreovneeraisl extepcehcnteiqduteosh eslpucshig naifis canretmlyoitne lpohwoetroipnlgetChOysVmIDog-r1a9pchuyr,v tehse,remspale cmiaelalysuirnesre, manodt estaerreeaosscwohpeicr evtishieorne taor eevnaoltumatea nthyep tlaarcgeest troisrke.c Tehivee symsetedmic aclanh emlpe.asTuarbel eth8e simumagme aprhizoetsopthleethdyrsomneogterachpnhoyl soiggynaflo, rskthine tdeemtepcetriaotnuroef, CanOdV sIDoc-i1a9l dinisdtaifnfecrinengt, wcohuinchtr iaerse. crucial parameters for tracing infected individuals. FFiigguurree 1100.. Covidrone schematic for sensing and disinfection [1]. Covidrone schematic for sensing and disinfection [1]. Micromachines 2022, 13, 1593 14 of 34 To handle the COVID-19 outbreak, smart healthcare will be developed with the use of detection technologies. Drones, the Internet of Things (IoT), fifth-generation cellular networks, and a DNN model are some of these technologies. Drones have historically been employed in defense-related tasks, such as remotely operating aerial missile launchers. However, in recent years, drones have been utilized for a variety of purposes, includ- ing monitoring climate change, taking pictures and videos, and delivering blood. Re- searchers [85] suggest employing numerous UAV swarms to address various COVID-19 issues, including social isolation, symptom surveillance, and sanitization. For performance and security, they advocate the use of 6G, blockchain, and software-defined networking (SDN). Robots and drones were used by the authors of [9] to monitor COVID-19 hotspots and offer medical assistance where it would be required. Their platform also supports a number of COVID-19 activities, including sanitization, thermal imaging, crowd monitoring, and control. The viability of such systems’ operations is still in doubt because they may need extensive network resources and coordination when implemented in relatively broad locations, such as a village. In a recent study [86], the authors focused on drone-swarm technology to limit the spread of COVID-19. They proposed a system architecture that can identify the infected cases by estimating their biomedical parameters. They considered sev- eral techniques such as remote photoplethysmography, thermal measures, and stereoscopic vision to evaluate the target risk. The system can measure the image photoplethysmog- raphy signal, skin temperature, and social distancing, which are crucial parameters for tracing infected individuals. Table 8. Drone technology used for detection during the COVID-19 pandemic. Reference Country Purpose The Australian Department of Defense and the University of South [87] Australia Australia are working on drone technology to detect breathing,heartbeat, temperature, and infectious respiratory conditions through sensors and integrated cameras Researchers from the Indian Institute of Technology (IIT) designed an [88] India IR-based drone for thermal screening and identifying COVID-19 suspected cases [89] China Jiangxi province, China, used drones to check the temperature ofpeople standing on balconies during lockdown In New York, Dragonfly’s pandemic drone is used to detect COVID-19 [90] USA infectious conditions including coughing, sneezing, and temperature through integrated sensor and computer vision technologies 5. Emerging Technologies to Fight against COVID-19 This section discusses several emerging technologies which assist in fighting against COVID-19. At present, several technologies are being used to combat COVID-19, includ- ing blockchain [55,91], smart-phone applications [92–100], wearable sensing [101–108], artificial intelligence (AI) [109], robots [110], deep learning (DL) [111–114], virtual re- ality (VR) [115,116], and edge computing [117,118]. Figure 11 presents an overview of these technologies. Micromachines 2022, 13, 1593 15 of 35 Table 8. Drone technology used for detection during the COVID-19 pandemic. Reference Country Purpose The Australian Department of Defense and the University of South Australia are working on [87] Australia drone technology to detect breathing, heartbeat, temperature, and infectious respiratory conditions through sensors and integrated cameras [88] India Researchers from the Indian Institute of Technology (IIT) designed an IR-based drone for thermal screening and identifying COVID-19 suspected cases [89] China Jiangxi province, China, used drones to check the temperature of people standing on balconies during lockdown In New York, Dragonfly’s pandemic drone is used to detect COVID-19 infectious conditions [90] USA including coughing, sneezing, and temperature through integrated sensor and computer vision technologies 5. Emerging Technologies to Fight against COVID-19 This section discusses several emerging technologies which assist in fighting against COVID-19. At present, several technologies are being used to combat COVID-19, including blockchain [55,91], smart-phone applications [92–100], wearable sensing [101– 108], artificial intelligence (AI) [109], robots [110], deep learning (DL) [111–114], virtual Micromachines 2022, 13, 1593 reality (VR) [115,116], and edge computing [117,118]. Figure 11 presents an overview of1 5 of 34 these technologies. FiguFreig 1u1r. eSo11m.eS oinmgeninioguesn tieocuhsntoelcohgnieosl otog ifeisghtot afigahitnastg CaiOnsVtICDO-1V9.I D-19. 5.1. B5.l1o.ckBclhoaciknc hain BlocBkclohcakinch taeicnhtneoclhongoyl oisg ymiaskminagk isnugbssutabnsttiaanl tgiaalingsa idnused utoe ittos iptsroporfo ooff ocfocnocnecpetp atnadn d use use ccaasseess inind idffifefreernetnitn idnudsutrsiterise.sT. rTursut satn danrdo bruosbtnuestsnseasrse athree ktheye fkeeaytu freeastoufreths isoft etchhins ology techonfofleorginyg odffeecrienngt rdaeliczeendtrsatloizreadg es.toItraegme.p Iot wemerpsobwoethrsp baottihen ptastaienndtsm aneddi mcaeldsiecravli sceerpvircoev iders provinidaerhse ailnt hcaa rheeeanlvthircoanrem eenntvtihroronumgehnst ectuhrreoudgahta tsreacnusraec tidoantsa. Ittrcaannsascutbiosntasn. tiIat llycarne move subsptahnytsiiaclalyl larebmorofvoer mpheydsiiccaall sltaabffoar nfdorc omnesiddiecaral bsltyafefl iamnidn actoensstihdeerinabfelcyt ieolnimriisnka.teTsh ethien tegra- infecttioionno fribslko.c kTchhea inintteecghrantoiolong yocf anblsoicgkncihfiaciann ttleycehnnhoalongcye ecpaind esmigicnipfirceavnetnlyti oennahnadncceo ntrol. This combination can ensure the reliability of collected data [91]. As most of the data are gathered from the media, public, and hospitals and are not privacy-preserved, blockchain technology causes COVID-19 data to be further improved and secured. It is not possible to further modify the transaction data stored in a blockchain. A blockchain can also secure data from cheating or hacking. It also ensures authentication and accountability to support data availability, integrity, and discretion. Moreover, a blockchain can properly work even if it partially fails. It offers several advantages such as transparency, immutability, and security. These features of decentralization and traceability make a blockchain unique as compared to other existing technologies. Thus, blockchain features can assist in data collection and perform further reliable analysis of COVID-19 data. Figure 12 outlines the use of blockchain technologies to tackle pandemic challenges through multi-drone collaboration [55]. The proposed architecture is based on four components. Different drones are used at different locations to perform various tasks. The blockchain can assist in securing each operation by keeping track of each drone’s operation. Each drone can access the locations of other drones, which can avoid collisions. In addition to the numerous advantages of utilizing blockchain technology to address COVID-19 pandemic concerns through multi-drone collaboration, there are a few limitations. The first are legal issues that must be resolved by various parties, such as international health organizations, country leaders, and international policymakers, in order to introduce new regulations regarding health policy; data sharing; digital health-service-related policy; and issues related to digi- tal inequality, digital connectivity, and the digital divide that exists mainly in developing nations. Scalability is the second and most significant concern. Typically, drones have limited computational and memory capabilities [119]. As the number of transactions on Micromachines 2022, 13, 1593 16 of 35 epidemic prevention and control. This combination can ensure the reliability of collected data [91]. As most of the data are gathered from the media, public, and hospitals and are not privacy-preserved, blockchain technology causes COVID-19 data to be further improved and secured. It is not possible to further modify the transaction data stored in a blockchain. A blockchain can also secure data from cheating or hacking. It also ensures authentication and accountability to support data availability, integrity, and discretion. Moreover, a blockchain can properly work even if it partially fails. It offers several advantages such as transparency, immutability, and security. These features of decentralization and traceability make a blockchain unique as compared to other existing technologies. Thus, blockchain features can assist in data collection and perform further reliable analysis of COVID-19 data. Figure 12 outlines the use of blockchain technologies to tackle pandemic challenges through multi-drone collaboration [55]. The proposed architecture is based on four components. Different drones are used at different locations to perform various tasks. The blockchain can assist in securing each operation by keeping track of each drone’s operation. Each drone can access the locations of other drones, which can avoid collisions. In addition to the numerous advantages of utilizing blockchain technology to address COVID-19 pandemic concerns through multi-drone collaboration, there are a few limitations. The first are legal issues that must be resolved by various parties, such as international health organizations, country leaders, and international policymakers, in order to introduce new regulations regarding health policy; data sharing; digital health-service-related policy; and issues related to digital Micromachines 2022, 13, 1593 inequality, digital connectivity, and the digital divide that exists mainly in deve1l6oopf i3n4g nations. Scalability is the second and most significant concern. Typically, drones have limited computational and memory capabilities [119]. As the number of transactions on ththeeb blolockckchchaianinr irsiesse,st, htheen netewtwoorkrk’s’st rtaraffiffcicb beceocommesesi nincrceraesaisningglylyh heaevavy.yE. Evvereyryn nododeeo onnt hthee bblolcokcckhcahianinis irse qrueqirueidretdo ktoee pkeaelpl vallli dvaatleidatrteadn satrcatniosnasc,tiwonhsic, hwphriecshe nptsreasechnatsll ean gcheaglilvenenge thgeivbelnoc tkhes ibzleoacnkd sitzime aenidn tteirmvea linreteqruviarel dretqougireende rtaot egeanneerwateb alo nckew[1 b20lo].ck [120]. Figure 12. Application of blockchain for multi-drone collaboration: (A) broadcasting messages, (B) Fipgruorveid1i2n.g Aprpoptleicctaitoino nmoafskbsl,o (cCk)c hparoinvifdoirngm muletid-idcraol nseupcpollileasb oarnadt isopnr:ay(Ain)gb droisaindfceacsttainngt, (mDe)s csraogwesd, (Bm) opnroitvoirdiinngg apnrdo tveectriiofyninmga tshkes, i(dCe)nptirtoyv (imdiondgifmieedd ifcraolmsu [p55p]l)i.e s and spraying disinfectant, (D) crowd monitoring and verifying the identity (modified from [55]). 5.2. Wearable Sensing Wearable technologies are developed with the integration of wearable sensors and the human body. These technologies can be found in the form of glasses, helmets, wristbands, and watches. These wearable technologies can perform various tasks in lifestyle, fitness, and healthcare [101]. In healthcare, they are used to monitor patients’ health records. IoT-enabled wearable devices include IoT-Q-Band [102], smart glasses [103], and smart helmets [104]. Figure 13 shows different wearable devices used for COVID-19. Recently, some studies have discussed the emerging wearable interface for human–drone integration to perform various tasks such as search and rescue [121], personal identification [122], and combatting pandemics [9]. The work presented in [122] can be used to trace people for monitoring, surveillance, and security purposes through drone technology. For instance, users, patients, or suspected cases can be integrated with wearable sensors such as smart bracelets connected to the internet. The IoT data can be retrieved from user wearables captured by a drone for personal identification. In [9], the authors proposed a UAV-assisted smart healthcare framework for social distancing, sanitization, monitoring, and data analysis for COVID-19. The proposed system collects data through thermal image processing, mobility sensors, or wearable sensors. The gathered data are further processed through a multilayered framework for data analysis and decision-making. The proposed approach is demonstrated through simulation and implementation. In the implementation, it is stated that a large distance can be covered within a short time through this drone-assisted smart healthcare architecture for COVID- 19. An overview of this proposed drone-assisted person monitoring architecture through wearable sensing technology is presented in Figure 14. Wearable sensors are used to Micromachines 2022, 13, 1593 17 of 35 Micromachines 2022, 13, 1593 17 of 34 5.2. Wearable Sensing Wearable technologies are developed with the integration of wearable sensors and mthoen ithour ma paant iebnotduyn. dTerhoesbes ertevcahtinoonl.oDgrioens ecsacna nbceo llfeocutnpda tiienn ttdhaet afofrromm othfe sgelawsseeasra, bhleesl,mets, awndritshtbeadnadtas,a raentdh ewn asttocrheeds.i nTdhreosnee wmeamroabryl.e Mteuclhtinpolelosgerivese rscaanre puesrefdortomf ovrwaraiordusth teasseks in stliofreesdtydlaet,a ftiotnbeisgsd, atnad. T heaslethsecravrer s[1u0s1e]c. loInu dh,efoaglt,hacnadree,d tgheecyo marpeu utisnegdt otop rmocoenssi,tomro pdaetl,ients’ phroefialeth, a rnedcaonrdalsy. zIeotTh-eesneadbaltead. Awftearrparbolpee rdaenvaiclyesi si,nrcelfiundeed dIoaTta-Qar-eBsahnadre d[1w02it]h, shmosapritt aglslasses th[1ro03u]g, hapnrdo psemr aprotl ichielsmaentds r[e1g0u4l]a.t iFoingsubrye t1h3e gsohvoewrns mdeinfftearenndt mweedaicralbbleo adrdev. Tichees duastead for aCreOsVecIuDr-e1ly9.t rRaencsefenrtrleyd, stoo mee dsitauldstieasff hwahveen edvisecrursesqeudir ethde. Tehmeeprrgoipnogs ewdefarraambelew ionrtkercfaance for bheuumseadnt–odhroannde leinthteegCraOtiVoInD -t1o9 ppaenrfdoermi cvsaitruioatuios nt.aRskegs arsduicnhg oaus rsdeiasrccuhs saionnd, wreesacruabel e[121], dpeevriscoesnahla ivdeednetimfiocantsitornat [e1d22th],e airnpdr ocommisbeadttuinrign gpathnedCemOiVcsID [9-1].9 Tphaen dweomrkic ;pnreosneenthteedle isns ,[122] sceavner bale liumseitda ttioo ntsramceu spteboepolev eforcro mmoen. iTthoreinmga,j osurirtyveoifllwanecaer,a abnledd seevciucersitayr epucurprroesnetsly thinrough pdrorotontey pteecdhenvoelloogpym. eFnotr. Iinntsetagnractein, ugsweresa,r pabalteiednetvs,i coers sinutsopdecrotende scatoseasi dcainn tbhee iCnOteVgrIDat-e1d9 with ewpiedaermabiclei sseanssiogrnsi fiscuant problem in and of itself. In addition, issues such as consuacceptability, security, cehth aicss ,samnadrtb ibgradcaetaleptsr ocbolnemnescrteelda tteod tthoew ineaterarnbelet.t eTchhen oIoloTg ydamt maeu sc r tan be breeatrdidevreesds efdrotmo i umsperro wveeathraebalcetsu acal pustueraendd bfyu nac dtioronnaeli tfyoro fptehressoendael vidiceens.tification. Micromachines 2022, 13, 1593 18 of 35 acceptability, security, ethics, and big data problems r elated to wearable technology must be addressed to improve the actual use and functionality of these devices. FFigiguurere1 31.3V. aVraioruiosuws ewareaabrlaebsleen sseinnsgindgev diceevsicfoesr CfoOr VCIODV-1I9D. -19. In [9], the authors proposed a UAV-assisted smart healthcare framework for social distancing, sanitization, monitoring, and data analysis for COVID-19. The proposed system collects data through thermal image processing, mobility sensors, or wearable sensors. The gathered data are further processed through a multilayered framework for data analysis and decision-making. The proposed approach is demonstrated through simulation and implementation. In the implementation, it is stated that a large distance can be covered within a short time through this drone-assisted smart healthcare architecture for COVID-19. An overview of this proposed drone-assisted person monitoring architecture through wearable sensing technology is presented in Figure 14. Wearable sensors are used to monitor a patient under observation. Drones can collect patient data from these wearables, and the data are then stored in drone memory. Multiple servers are used to forward these stored data to big data. These servers use cloud, fog, and edge computing to process, model, profile, and analyze these data. After proper analysis, refined data are shared with hospitals through proper policies and regulations by the government and medical board. The data are securely transferred to medial staff whenever required. The proposed framework can be used to h andle the FFCiiggOuurrVee I11D44..- 1D9rro onnpeea--aansssdsiisestmteeddic pp eerssrisotonunam tmoiononintoi.t roiRrnigneggsy assrytdesmtienmugs uinosgiunwrg ewadreaiasbrclaeub-ssleesn-isoseonnr,s doarwt adea(amtraao (dbmilfieoe ddidffiereovdm icfr[eo9sm] ). have [9d])e. monstrated their promise during the COVID-19 pandemic; nonetheless, several 5l.i3m. Aitrattiifiocniasl Inmteullsitg enbcee overcome. The majority of wearable devices are currently in 5p.3r. oAtAortrtiytfipficiecai lda Ilenivtneetllleiolglpeignmecene ncet. (IAnIt)egisratpinogw werefuarlatbecleh ndieqvuieceasn idnttoo dlrwonheicsh tom aikde sinc othmep CuOteVrsID-19 thepinikdAearmntidfiiccl eiiaasl r anin .stTeiglhlniigsifetinecaccehn nt( AoplIro)o gibsy laem mpao kiwense arpfnruedld toiecfct hiiotnsnieqslufo.e f Iapnna adttd etdronoitsli .owAnh,I ii[cs1hs0 um9e]ashk asesus bccheoe manspe ucxotteenrns-u mer think and learn. This technology makes predictions of patterns. AI [109] has been extensively growing, and the research community has started integrating AI algorithms into drones and robotics for medical imaging [123–125] and other healthcare applications. AI can be implemented to predict the outbreak or spread of COVID-19. AI-based analysis models can be used to verify the statistical data about COVID-19. This can remove the healthcare staff burden of performing various duties such as the medical examination of patients [126]. Furthermore, AI can empower drones to enhance performance and productivity to fight against COVID-19 in multiple scenarios. COVID-19 has drastically enhanced the usage of AI in various environments. The potential of this technology can be validated in Scopus database where more than 1000 articles were published as of 1 March 2021 containing keywords “Artificial Intelligence and COVID-19” [5]. Like edge computing, AI has the potential to handle huge data. It makes use of big data analytics to deal with data. It can be integrated with UAVs to gather data. AI-empowered drones have been used to trace hotspot locations. UAVs and integrated sensors can gather data to perform necessary actions without human need. This promising technology has shown remarkable results in the diagnosis and treatment of infected patients as it can perform several tasks to offer substantial support to medical staff. Drones integrated with AI technology can support various features such as transportation, physical inspection, and surveillance [127]. In [128], the authors proposed an IoT/AI-based framework to monitor, track, and fight against COVID-19. The proposed IoT/AI-based system includes two components for monitoring and drone-assisted sterilizing. It includes a thermal camera that can be integrated into a helmet. The thermal or detected images are processed using an AI algorithm. The drone can be called once an infected case is detected. In a recent study [129], the authors proposed a conceptual model (shown in Figure 15). It is based on a drone, an AI framework, RF links, and a GCU. The proposed system can serve multiple tasks such as face mask detection, anti-social-distancing behavior, and violating rules for quarantine at home. In addition, it can also trace the activity of infected people and perform thermal imaging to check Micromachines 2022, 13, 1593 18 of 34 sively growing, and the research community has started integrating AI algorithms into drones and robotics for medical imaging [123–125] and other healthcare applications. AI can be implemented to predict the outbreak or spread of COVID-19. AI-based analysis models can be used to verify the statistical data about COVID-19. This can remove the healthcare staff burden of performing various duties such as the medical examination of patients [126]. Furthermore, AI can empower drones to enhance performance and produc- tivity to fight against COVID-19 in multiple scenarios. COVID-19 has drastically enhanced the usage of AI in various environments. The potential of this technology can be validated in Scopus database where more than 1000 articles were published as of 1 March 2021 containing keywords “Artificial Intelligence and COVID-19” [5]. Like edge computing, AI has the potential to handle huge data. It makes use of big data analytics to deal with data. It can be integrated with UAVs to gather data. AI-empowered drones have been used to trace hotspot locations. UAVs and integrated sensors can gather data to perform necessary actions without human need. This promising technology has shown remarkable results in the diagnosis and treatment of infected patients as it can perform several tasks to offer substantial support to medical staff. Drones integrated with AI technology can support various features such as transportation, physical inspection, and surveillance [127]. In [128], the authors proposed an IoT/AI-based framework to monitor, track, and fight against COVID-19. The proposed IoT/AI-based system includes two components for monitoring and drone-assisted sterilizing. It includes a thermal camera that can be integrated into a helmet. The thermal or detected images are processed using an AI algorithm. The drone can be called once an infected case is detected. In a recent study [129], the authors proposed a conceptual model (shown in Figure 15). It is based on a drone, an AI framework, RF links, Micromachines 2022, 13, 1593 19 of 35 and a GCU. The proposed system can serve multiple tasks such as face mask detection, anti-social-distancing behavior, and violating rules for quarantine at home. In addition, it can also trace the activity of infected people and perform thermal imaging to check tteemmppeerraattuurreess iinn oorrddeerr ttoo ssttoopp tthhee sspprreeaadd ooff tthhee vviirruuss. .WWhheeththeer rAAI Iwwilill lbbe eanan efeffefcetcitvive etotoolo l aaggaaiinnsstt ffuuttuurreed disiesaeasessesa nadndp apnadnedmeimcsicdse dpeenpdensdhse ahveialyviolyn doant ad.aTtha.e Tdhaen gdearnisgethr aitsp tuhbalti c hpeuablltihc ishseuaeltshw iossuuldeso vweorurildde ocvoenrcreirdnes caobnocuetrdnsa taabporuivt adcya.taIt pisripvoascsyi.b lIet tihsa tpgoossviebrlne mtheantt s mgoavyecronnmtiennutse tmheaiyr ecxocnetpintiuoen atlhseuirr veexilcleapntcieonoaf lc isvuilrivaenislleavnecne aofft ecritvhileiaenpsi deevmenic hafatsere ntdheed . Tepheidreefmorice ,hwaso errnidesedo.v Tehr ethreefoerroe,s iwoonrorifeds aotvaepr rtihvea ceyroasrieonw oafr rdaantate pdr.ivacy are warranted. FFiigguurree 1155.. CCoonncceeppttuuaall aarrcchhiitteeccttuurree ooff tthhee pprrooppoosseedd mmooddeell AAII ffrraammeewwoorrkk [1[12299].] . 5.4. Deep Learning Algorithm for UAVs In [111], researchers have proposed a DL-based drone technology to monitor the public and determine if someone is wearing a mask or maintaining social distancing. If anyone is not following the preventive measures, then it immediately sends a warning to the closest police station. The proposed approach utilizes a drone, integrated camera, sensors, and the Yolov3 algorithm to monitor public places. The proposed system also provides instructions to wear a mask. This system can save human lives by mitigating the spread of COVID-19 by monitoring social distancing. However, this system will cause implications in crowded places by capturing each individual’s image. Figure 16 illustrates the use of DL-based UAV technology to monitor public places. In a recent study [112], authors briefly explained the DL approaches for COVID-19 medical image processing. The authors also discussed the implementation of DL to control the outbreak and crises, ensuring smart healthy cities. In [113], the authors proposed a low-cost security mechanism for UAV-aided mask detection through DL and IoT in order to minimize COVID-19. Micromachines 2022, 13, 1593 19 of 34 5.4. Deep Learning Algorithm for UAVs In [111], researchers have proposed a DL-based drone technology to monitor the public and determine if someone is wearing a mask or maintaining social distancing. If anyone is not following the preventive measures, then it immediately sends a warning to the closest police station. The proposed approach utilizes a drone, integrated camera, sensors, and the Yolov3 algorithm to monitor public places. The proposed system also provides instructions to wear a mask. This system can save human lives by mitigating the spread of COVID-19 by monitoring social distancing. However, this system will cause implications in crowded places by capturing each individual’s image. Figure 16 illustrates the use of DL-based UAV technology to monitor public places. In a recent study [112], Micromachines 2022, 13, 1593 authors briefly explained the DL approaches for COVID-19 medical image proce2s0 soinf g3.5 The authors also discussed the implementation of DL to control the outbreak and crises, Micromachines 2022, 13, 1593 ensuring smart healthy cities. In [113], the authors proposed a low-cost security mec 2h0a onfi s3m5 for UAV-aided mask detection through DL and IoT in order to minimize COVID-19. Figure 16. DL-based UAVs to monitor public places (modif ied from [114]). FigurIen 1 a6 .r DecLe-bnats setdu UdyA V[2s2 t]o, mthoen aituotrh pourbsl ipc rpolapcoese (dm oad difeieedp ftrroamn s[114]). Figure 16. DL-based UAVs to monitor public places (modified from [1fe1r4 ]l)e. arning (DTL) real-time frameInw ao rrke cteon ti dsteundtiyf y[2 o2]v,e trhcer oawutdhionrgs pdruoep otose dac ati vdieteies I p trsauncshf ear sl eaabrnnionrgm (aDl TbLe)h raevailo-tri mane d cforanmge n a recent study [22], the authors proposed a deep transfer learning ( TL) real-time frameeswtioornk. Ittwork too waididees thnnttiiffeyy fiorvst study that poveerrccrroowdiinngg duuroepe osttoo eadc ta monactiivviittiieess istourcisuchn ga h asa paproas abbnnoorcmha for ther all bbeehhaav dioetviorr eacntidon of overcrowding and coconnggeesstitoionn.. IItt waa stsh tthrhoeeu fifgirrhsst ta s ssttoucdiyayl t tmhhaaottn pitrrooorpinoosgsee sdy saat emmoo nn(SiittMoorrSiin)n gagn aadp pa rrUooaAaccVhh . ffToohrr etth haeue dtheeotterecsct utiiosonen d oDof fTo ovLve ferocrcrr ordowewcddiisniinoggnt -thmhrroaoukugignhhga aa ssnoodcc iiatahlle m woonantiitetoorr rciinnyggcl sesyy asslttgeeomr i(t(ShSMmS S())W aannCddA aa) UtUoAA sVig.. nTihhfieec aaaunuttthlhyoo rersns uhusasenedcd e DtDhTTeL Lifd ofeornrdt diefeiccciasisitoiioonnn-- bmyaa kkcihinnoggo asaninndg t tthhheee wbaeasttete rrf eccayytcculleree aasll.g gToohrrieitt hhpmro p((WosCeAd ))f rttaoom ssieiggwnnoiiffirickca acnnattlnlyy oeepnnehhraaantnecc eie n tchthheeai ldildeenenngtitifinifcgica ateitnoionvnib rboyync mchhoeonootsssiin ntggo tthdheee tbbeeecstst t offeevaaettruucrrreeossw.. Tdhhineeg pp rrthoopproooussegedh ffSrraaMmSee wcooomrrkkm ccauannn iocopapteeiroraantte e aiinnn d cUhchaAallVlelen vngigidninegoge fnervnamivrioernosm.n Amennet nsotvsto etrdov eidetewectt eoocftv teohrvicser orscwyrsodtweinmdgi nitshg r potrhuergsoehunSgteMhd S SinMco FSmi gmcuourmen mi1c7ua,tn iwoicnhatiaiconhnd s UhanoAwdV s vhUidoAweoV o fvvraiedmrecoeros f.wraAdmnineogs.v cAearnnv iboeevw esriovgfinetiwhfiic soafsn ytthlsytise c mosyniststreopmlrlee idsse tpnhrtreeodsuenigntheF dUi giAnuV rFe isg1eu7nr,sewi n1hg7 i,ac whndhs ihccoohwm sshpouhwtoinws g ophvaoerwrtcs r.oo vwedrcinrogwcdaninbge csaing nbiefi sciagnntilfyiccaonntltyro clloendtrtohlrloedu gthhrUouAgVh sUenAsVin gseannsdincgo amnpdu ctoinmgppuatirntsg. parts. FFigiguurree1 177. .U UAAVVa annddS SMMSSt otoc oconntrtorol lC COOVVIDID-1-919s pspreraedad[ 2[2 2]2.]. Figure 17. UAV and SMS to control COVID-19 spread [22]. 5.5. VR Technology 5.5. VVRir Ttuecahl nroeloagliyt y (VR) [130] refers to an extensive range of technical interventions. ThoseV ivraturya lb reetawlieteyn ( VlaRrg) e[ 1si3m0]u rlaetfoerrss , tfola ta-ns cerexetenn psirvoeje cratonrgse, aonfd t ehcehandi-cmalo iunntetervde dnitsiopnlasy. s (THhMosDe sv)a [r1y1 5b]e.t Twheeeyn blarringge sthime uulsaetrosr sth, frloaut-gshcr ecoenm ppruotjeerc-tboarsse, dan gdr ahpehaidc-sm inotuon atend i mdimspelrasyisv e m(HuMltiD-sse)n [s1o1r5y]. sTcheenya rbiroi.n Eg xtihseti unsge rVsR th hroeaudgshe ctso mofpfeurt earf-fboarsdeadb gler aapnhdic ssa ifnet or eahna ibmilmitaetrisoinv ea t hmoumltei-. seVnRs oirsy ussceedna rinio .a E cxoismtinpgu tVerR-b haesaedds eptrso ogfrfaemr atfofo rgdeanbelrea aten da sparfee freerhabableil istaimtiounla atet d ehnovmireo. nVmRe nits. uPseeodp lien aca nco mpaprutitceirp-baates edin parongyr atmas kt o ing enreearal teti ma ep ruesfeinragb lae sdiimsturilbatuetde d wenhvitireobnomaredn. tT. hPise otepclhe ncoalong yp ahratisc bipeactoem ien anan iny tetgarsakl pina rtr eoafl t etliemmee duisciinnge [a1 31d]i satsr iibt uptleady s aw kheitye brooaler di.n T ehnish atencchinnogl omgeyd hicaasl b kencoomwele adng ein, teenghraaln pcainrtg o df itaeglenmosetdicic isnkeil [ls1,3 1a]n ads fiot sptlearyins g sau krgeiyc arlo lsek iilnls .e nThhaisn ctiencgh nmoelodgicya lh kasn othwel epdogtee,n etinahl atnoc hinagn ddliea gCnOosVtiIcD s-1k9il las,s aitn dca fno spterorivnigd e aswuragriecnale sssk iallms.o Tnhgi sp teeocphlneo tlhogroyu hgahs itmhea gpeo taecnqtuiaisl ittoio hna innd oler dCeOr VtoID a-l1le9v aias teit tchaen ipmrpovaicdt eo f tahwe avreirnuess.s VamR opnrgo pviedoepsl ea t hsruoiutagbhl ei meangvei raocnqmuiesnitti ofno ri ne sotradbelris thoi nagll evviidaeteo thcael lism bpeatcwt eoef n the virus. VR provides a suitable environment for establishing video calls between Micromachines 2022, 13, 1593 20 of 34 5.5. VR Technology Virtual reality (VR) [130] refers to an extensive range of technical interventions. Those vary between large simulators, flat-screen projectors, and head-mounted displays Micromachines 2022, 13, 1593 (HMDs) [115]. They bring the users through computer-based graphics into an immersive 21 of 35 multi-sensory scenario. Existing VR headsets offer affordable and safe rehabilitation at home. VR is used in a computer-based program to generate a preferable simulated envi- ronment. People can participate in any task in real time using a distributed whiteboard. doctoTrhsis atencdh npoalotgieynhtass ibne cqomuaeraannintitneger aolrp harot sopf tietalelms.e dIti ccinaen[ 1e3n1h] aasnict ep lathyse apkseyycrholoeliongical side of patieennthsa, necsinpgecmiaedlliyca tlhken oqwuleadrgaen,teinnhea npcaintigednitasg,n toos tgicivskei lltsh, eamnd cfoosntefriidnegnsucerg tichaal ts ktihlls.This technology has the potential to handle COVID-19 as it can provide awareness amoneg doctors are noticpineogp laellt htrhoeu gvhitimala gsiegancsq uoisri taionnyi npoorsdseirbtloe aclloemviaptelitchaetiiomnpsa cot fo fCtOheVvIirDu-s1. 9V R[1p3r2o]-. It can also ensuvreid vesiratusuailt avbilseietns vwirohnicmhe nctafno rresdtaubcliesh tihneg vpiadteioencatl’ls bnetewde efnodr oacstsoirstanndcpea otire nthtsein need for any familqyu amranetminbeeorr htoos ppithalyss. iIctacallnye nvhiasnitc eththee phsoyschpoitloagl icaanl sdid ereodf upacteiesn ttsh, ees pceocsiat lloyft hteravel to the hospqituaalr.a Tnthinee kpeatyie bntesn, teofgitisve them confidence that theor any possible complicatio onfs oufsCinOgV ItDh-i1s9 t[e1c3h2]n. Iotlco dgoyct oforsra CreOnoVtiIcDin-g1a9l latrhee vililtal signsan also ensure virtual visits whuicshtrcaanted in Figure 18. Trheidsu ctreatnhesfpoartimenat’tsivneee tdefcohrnaossliostgaync ecaonr thbee nienetdefgorraatneydf awmiitlhy mUeAmVbesr ttoo pehnyssuicraell ya direct and broadvi svititehwe hofs paitcatliavnitdieresd. uIcte sist hbeacsoestdo fotnra vae lhtoeathdesheot sipnitaelg. Trahteekde ywbeitnhe fistes nofsousrisn gas shown in Figurthei s1t9e.c hTnhoelo goypfeorraCtOorV IcDa-n19 sairme ipllulyst ruatseed hineFaidgu mreo18v.eTmhiesntrtasn stfoo rcmoantitvreotle cthhneo lUogAyV’s camera. The ccaanmbeeriant ecgarnat esdcwanit hthUeA Vpsetoopenlesu, raenadd irveicdt aenod wbroilald bveie wtraonf ascmtiviitttieesd. Itt is based on aheadset integrated with sensors as shown in Figure 19. The operator can simhplryouusgehh eaa dVR headset. This msyosvteemen ctasnto bceon utrsoeldth feoUr ArVea’sl-ctaimeera d. Tehteecctaimoner oa fc apnesocpanlet.h Re peecoepnletl,ya,n ad vCidaenoawdiilaln company, Dragboentfrlayn,s mhiattse dsttharrotuegdh awVoRrhkeiandgs eto.nT hidsrsoynstem tecacnhnbeoulosegdyf oarsr eal -tsimcreedeenteinctgio ntofol to detect temppeeroaptlue.rRee acenndtl yi,nafCeacntaioduiasn croemsppiarnayt,oDrrya gcoonnflyd,ihtaiosnstsa r[t1ed33w,o1r3k4in].g Tonhder ohneeatletchhcnaorloeg ystaff can use this taescahsncoreleongiyng toto ol to detectThe healthcare starfefmcanotuesleyt c tem hios n p ten eeracturchnot loto e,a cnodminmfecutinoiucsartees pirgy to remotely connewcti attho,ry conto, c oamnmd dtritune ions [ icaatte pwa 13ti3e,134ith,nants ]. d. treat patients. Figure 18. Key benefits of using VR technology for COV ID-19 (modified from [135]). Figure 18. Key benefits of using VR technology for COVID-19 (modified from [135]). Figure 19. VR-based monitoring through UAV (modified from [116]). Micromachines 2022, 13, 1593 21 of 35 doctors and patients in quarantine or hospitals. It can enhance the psychological side of patients, especially the quarantine patients, to give them confidence that the doctors are noticing all the vital signs or any possible complications of COVID-19 [132]. It can also ensure virtual visits which can reduce the patient’s need for assistance or the need for any family member to physically visit the hospital and reduces the cost of travel to the hospital. The key benefits of using this technology for COVID-19 are illustrated in Figure 18. This transformative technology can be integrated with UAVs to ensure a direct and broad view of activities. It is based on a headset integrated with sensors as shown in Figure 19. The operator can simply use head movements to control the UAV’s camera. The camera can scan the people, and video will be transmitted through a VR headset. This system can be used for real-time detection of people. Recently, a Canadian company, Dragonfly, has started working on drone technology as a screening tool to detect temperature and infectious respiratory conditions [133,134]. The healthcare staff can use this technology to remotely connect to, communicate with, and treat patients. Micromachines 2022, 13, 1593 21 of 34 Figure 18. Key benefits of using VR technology for COVID-19 (modified from [135]). Micromachines 2022, 13, 1593 22 of 35 FFigiguurree1 199.. VVRR--bbaasseedd mmoonniittoorriinngg tthhrroouugghh UUAAVV ((mmooddifiifieedd frforomm [1[1161]6)].) . 5.6. Edge-Computing-Based Drone Technology 5.6. Edge-Computing-Based Drone TechnologyIoT objects offer several services (autonomous driving, traffic management, location trackIoinTg,o baujegcmtseonftfeedr sreevaelirtyal (sAerRv)i, ceetsc.()a uwthoincohm uosuuasldlyr inveinedg , etrxateffinscivmea dnaatgae-mpreoncet,ssloincga,t ion trleaacdkiinngg , taou ghmigehn tceodmrpeualtiattyio(nAaRl )r,eestocu.)rwceh dicehmuasnudasl.l yMnoebeidlee xetdegnes ivcoemdpatuat-ipnrgo c(eMssEiCng) ,ilse ad- inpgrotpoohseigdh tcoo mefpfeucttaivtieolyn aolvreerscooumrcee tdheism cahnadlsle.nMgeo.b MileEeCd gseercvoemrsp cuatnin gof(fMloaEdC )eixstepnrsoipvoe sed to co emffpeucttiavteiolynaol vtearsckosm freotmhi sIocTh aollbejnecgtes.. MThEeCy sinertevgerrastec aendogfefl odaevdiceexst efnosri vreeaclo-tmimpeu dtaattiao nal tapsrkoscefsrsoimngI othTrooubgjehc tsse.nTshoresy ainndte gmriactreoecodngterodlleevrisc eosr fmoricrreoapl-rtoicmesesdorast.a Hporowceevsesrin, gdetlharyosu gh saendso crosmanmdunmicicartioocno notvreorlhleerasdo arrem sictirlol pcrrioticceasls coorns.ceHrnosw. Weviethr, dtheel apyosteantdiacl oomf lmowun ciocsatt ion oavnedrh heiagdh amreosbtiilliltyc,r iUtiAcaVl sc ocnacne ornvse.rcWomithe tthespeo ctehnatllieanl goefsl obwy coopsetratnindgh aigsh MmEoCb isleitryv,eUrsA Vs c[a1n17o]v. eTrchoem oeftfhloeasdeicnhga ldleencgiseisonbsy ofopre rsauticnhg asyssMteEmCs sienrcvleurdse[ 1e1x7e].cuTthioeno ffldeolaadyisn, gpdoewciesri ons focornssuucmhpstyiosnte, masndin csleurdveice xelactuenticoyn. dInel aay sr,epceonwt erwcoornk,s uamutphtoiorsn ,haanvde saedrvdirceessleadte nthcye. In ainrecocerpnotrwatoiornk ,oafu UthAoVrs ahnadv eedagded croesmsepduttihnge iwnhcoicrhp coarna tmiointigoaftUe tAhVe schaanldlenegdegse ocfo omnspiutet ing wcohmichpuctantiomn,i tdigaatate ptrhoececshsainllge,n cgoensnoecftoivnistyit,e acnodm laptuentactyio [n1,18d]a. tTahper opcroepssoisnegd, acopnpnroeaccthiv ity, acnadn labtee nucsyed[1 i1n8 ]c.oTnhtaeinpmroepnots ezdonaep pdreoteacthiocna nanbde uthserdminal csoenntsaiingm. eTnhtez opnroepdoesteedc tidoneaa nd thcreeramteasl tsheen psoinssgi.bTilhitey pofr oupsionsge driodneeas ctore eaxtpeslotrhee thpeo CssOibVilIiDty-1o9f puasnindgemdirco snmesootothenxepsslo froer the CsOomVeID so-1c9iapl adnudtiems. iIcns tmhioso styhsntemss, feodrgseo mcoemspouctiianlgd pultaieyss. aI nmtahjiosrs ryosltee min, setdregnegcthoemnpinugt ing pdlaaytas parmocaejsosrinrog lewiinthsoturet nugtitlhizeinnign gbadnadtawpidrtohc eosvseinr gthwe iitnhtoeurnteut.t iIlti zcionngsbidaenrds wthied tfhleoxivbelre the indteeprlnoeytm. eItntc oonf seiddgeer sntohdeesfl aenxdib mleudlteipplloe ydmroennets oufseindgg e5Gn ospdeecstraan. dThme uplrtoipploesdedro snyessteums ing 5aGrcshpiteeccttruar.eT ihne sphroowpno siend Fsiygsutreem 2a0r. cMhiuteltcitpulere dinrosnheos wcanni nbeF ilginukreed2 0fo. rM dualttaip slteodrargoen eosr can bceomlinmkuendicfaotriodna wtaitsht oar caegnetroarl ccloomudm suernviecra.t Tiohnisw seirthvear icse cnotnranlecctleodu wd istehr tvheer .usTehrsis ins ethrvaet r is csopnenceifcicte adrewa iwthhethree duesteercstioinn tish abteisnpge pciefirfcoarmreeadw. Thheirse adpeptreocatciohn cains bbee iunsgedp eornf osrtrmeeetds .toT his adpepprlooayc hnucamnebroeuuss eddroonness trfoeer tCs tOoVdIeDp-l1o9y vniuctmime rdouetsedctrioonne. sTfaobrleC O9 VsuIDm-m19arviizcetsim thdee utescet ion. Tcaabslees 9osfu dmrmonaersi zfeosr thcoemubseatcinagse spaonfddermonicess fwoirthc otmheb aitnitneggrpaatinodne mofi cdsifwfeirtehntth eeminetregginragt ion otfedchifnfeorloegniteesm. erging technologies. FFigiguurree2 200.. Edge–drone applliiccaattiioonn sscceennaariroio (m(modoidfiefide dfrofrmom [11[181])8. ]). Table 9. Use of drones with different integrated technologies. Reference Integrated Technology Use Case Internet of Mobile Things, [1] Deep Neural Network Rapid testing, quick delivery of medical kits [9] Wireless Sensor Networks Sanitization, thermal image collection [20] YOLO-v3 Social distancing [21] Machine Learning Medical source distribution [22] Visual Sensing, DTL To monitor overcrowding in order to control COVID-19 spreads [118] Edge Computing Containment zone detection with offline data processing and computation [131] Could Virtual Reality Role in the rehabilitation after COVID-19 infection [136] IoT Spread reduction Micromachines 2022, 13, 1593 22 of 34 Table 9. Use of drones with different integrated technologies. Reference Integrated Technology Use Case [1] Internet of Mobile Things, DeepNeural Network Rapid testing, quick delivery of medical kits [9] Wireless Sensor Networks Sanitization, thermal image collection [20] YOLO-v3 Social distancing [21] Machine Learning Medical source distribution [22] Visual Sensing, DTL To monitor overcrowding in order to control COVID-19 spreads [118] Edge Computing Containment zone detection with offline data processing and computation [131] Could Virtual Reality Role in the rehabilitation after COVID-19 infection [136] IoT Spread reduction [137] Blockchain, 5G networks Vaccine distribution [138] Blockchain, 6G networks Mass surveillance [139] Internet of Things Sanitization [140] Blockchain, AI Pandemic supervision [141] Convolution Neural Network (CNN) Crowd density [142] Deep Learning Fast detection of medical face masks [143] YOLO-v3 Localization, navigation, people detection, crowd identification, and socialdistancing warning [144] Hybrid Reinforcement Learning Optimized path planning for UAV COVID-19 test kit delivery system [145] IoT Identifying COVID-19 patients for providing medical services using drones [146] 6G and Blockchain Proposes a generic scheme to integrate UAV, 6G, and BC for securedvaccine distribution [147] Machine Learning Crowd surveillance [148] AI, IoMT For monitoring and supplying COVID-19 patients [149] Radar Sensing Detection of respiratory disorders in COVID-19 patients [150] Blockchain Secure and QoS-aware drone delivery framework [151] Reinforcement Learning,Wireless Network COVID-19 tracing 6. Open Challenges, Opportunities, and Future Work There are several challenges and security issues that must be addressed in future works related to UAV-based COVID-19 applications. • Patient Health History: The existing works focus on camera-based UAVs for scanning and capturing the user for authentication tasks. In the future, researchers can use advanced processing tools and cameras which keep a record of previous history and reports of the patient. This can significantly assist medical staff in knowing previous conditions and recommending medical advice accordingly. • Reconfigurable Platforms: There is a need to integrate reconfigurable platforms such as FPGAs which can add, modify, or remove any function virtually. This can ensure availability and flexibility and enhance performance. These platforms can assist in optimal resource allocation and remove the burden of some hardware payloads. • Integration with Healthcare Databases: Future studies should focus on integrating UAV-assisted COVID-19 screening and detection systems with national or interna- tional health monitoring databases. This can ensure real-time and accurate updates about positive cases, asymptomatic cases, active cases, and number of deaths; highlight high-risk areas; and assist regulatory bodies in taking immediate actions to contain the spread of disease through proper planning such as implementing lockdowns. • Drone Navigation in Challenging Environments: In some scenarios, the UAVs are required to operate in densely populated areas or thick forest-covered areas. Un- even atmospheric conditions such as haze, fog, and rain can also severely impact the performance of UAVs. To overcome these issues, future works can incorporate computer-vision-based strategies for the safe operation of UAVs. There should be proper insurance liability due to damages caused by UAVs. Several media reports describe soft tissue injuries, eye loss, and severe lacerations due to UAV accidents. In addition to property damage and injuries due to UAV crashes, UAVs also cause acci- Micromachines 2022, 13, 1593 23 of 34 dents with aircraft and liability due to damaged goods and dropped cargo. Liability for UAV use also contains an enormous threat to individual privacy. • Health and Safety Risks: Using drones in medical facilities where they need to operate in close proximity to patients generates safety and health concerns. For instance, drone batteries must be safe and sealed to prevent any chemical damage. The drone rotors or wings must be guarded to avoid any accidents which can cause severe injuries. Future works should address these issues for the safe operation of UAVs. • Security, Privacy, and Safety Concerns Associated with Drone Use: Drones deployed in densely populated places, especially during COVID-19 and other pandemics, may create security, privacy, and safety concerns. Drones crashing on public land might pose a safety risk. This might happen due to a number of factors, including human error, a breakdown in machinery, a collision in flight, or a lack of proper maintenance. In addition, there is a significant danger of airborne accidents resulting in extensive devastation because of the congestion of airspace in bigger cities from commercial flights. Drones equipped with high-resolution cameras, sensors, and recorders can be piloted remotely for pinpoint surveillance. Using a drone equipped with malicious software, it is possible to collect sensitive information, monitor people’s movements, and create detailed profiles about them using wireless localization. Moreover, the navigation and communication units of drones are susceptible to a variety of security vulnerabilities [152]. Due to the open nature of unencrypted and unauthenticated GPS signals, they are easily spoofable [153]. GPS allows the navigation system of a drone to function [84]. Wi-Fi jamming is another conceivable assault that might result in the loss of control of the drone’s communication system, which would have severe repercussions for nearby individuals. Concerning is the fact that such attacks may be carried out using commercially available products. If the drone is running on an unprotected Wi-Fi network, a normal cell phone may suffice. In addition, as the number of drones continues to increase and wireless technologies continue to advance, security and privacy issues are growing. In addition, AI/ML and Edge-AI/ML-based UAV capabilities on drones need the implementation of security measures to prevent AI-related attacks. Model inversion and model extraction are two ML-as-a-service threats recently uncovered in drones. The training data are recreated from the model parameters via model inversion attacks to retrieve sensitive and private information. Model extraction attacks are dependent on model parameters obtained via model querying. Drones connected to 6G networks and supporting technologies can help mitigate the aforementioned challenges. Additionally, lightweight authentication methods, federated learning, and aerial blockchain technology will enhance the cyber- physical attack defense of drones. • Standardization: Despite the extensive emergence of UAVs, there is a dire need to devise standardizations from regulatory bodies for the operations of UAVs in geographic areas of different countries. A major hindrance in the widespread use of UAVs is the ambiguity or lack of significant standards and regulations for UAV operations, allowed airspace, allowed weight and size, allowed height, privacy or secrecy considerations, safety requirements, and characteristics. A lack of homogeneity in government rules for the implementation of UAVs can be observed. UAVs can affect the navigation of commercial airplanes. So, countries should implement regulations and rules for the proper operations of UAVs. For instance, in the US, the FAA is responsible for issuing certificates and air traffic regulations for UAVs. Similarly, international collaboration or coordination can also assist in introducing global rules, as different countries have different regulations and standards. • Resource Allocation: Due to UAV path constraints and battery limitations, resource allocation has become a crucial concern. It is noticed in three aspects: UAV hovering, local computing, and task offloading. Thus, designing accurate path planning can consequently compromise the operational cost and calculated performance. Intelligent deployment [154] and efficient resource allocation can improve fairness, coverage, Micromachines 2022, 13, 1593 24 of 34 task completion time reduction, cost reduction, power consumption reduction, and computational efficiency maximization. • Limited Energy: Another hurdle for UAV operation performance is power limitation, energy consumption, or limited battery life. Usually, UAVs are battery-powered and suffer from short battery life, generally below 1 h. UAV batteries are consumed for image analysis, data processing, wireless communication, and UAV hovering. Usually, UAVs need to travel over large areas and need to return multiple times to charging stations. In SAR operations, UAVs fly for longer time periods over disaster-stricken areas. Due to these limitations, a decision should be made as to whether UAVs carry out image or data analysis in real time or not. One possible solution is to form swarms of drones through coordination algorithms that can overcome the limitation of a single drone in terms of energy efficiency. Throughout the COVID-19 outbreak, drone swarms were employed for a wide range of purposes. While the majority of such drones were operated by humans, studies on drone swarm control schemes have demonstrated that autonomously piloted drones outperformed personnel in a number of activities. A recent study lays the groundwork for a paradigm that uses variable independence in drone handling during outbreak missions to improve overall performance. It also discusses the issues to consider while creating mixed- initiative platforms that swing between human control and autonomy during a task. Flexible autonomy guarantees that individuals who comprehend the pandemic’s social characteristics are involved in the control loop [155]. Other interesting approaches are the investigation of novel designs for recharging stations and the use of efficient wireless power transfer (WPT) methods such as laser power transfer (LPT). • Connectivity: It is critical for a FANET to provide network connectivity to drones and to or from other devices in order to be considered as a communication network. To stay connected, deployed UAVs must fly inside the covered region. However, the drone’s low battery is an impediment to a long-term mission, which might result in traffic gridlock with no access possibility. As a result, determining whether to add an innovative UAV to the UAV swarm or to remove a drone (due to low battery) is an issue to be addressed. • Atmospheric Conditions: In case of adverse weather conditions such as storms, rain, and wind, UAV deployment for different applications such as precision agriculture is difficult due to unwanted deviations in predetermined trajectories. Weather conditions also affect operation time, path elevation, UAV altitude, and flight direction. In natural disaster conditions, e.g., typhoons, hurricanes, or tsunamis, atmospheric conditions tend to be a cardinal challenge for UAV missions. In these detrimental conditions, UAVs cannot hover and cannot collect accurate readings or data, and they cannot operate in extreme conditions. Therefore, researchers should address the specifications and UAV capabilities to withstand these adverse weather conditions and complete weather-sensitive missions efficiently and safely. Specifically, wind speed should be taken into account for smooth UAV operations, and it should be involved in the UAV strategic mission plan and deployment phase. • Privacy: Another major concern about privacy arises with the use of UAVs. UAVs are incorporated with cameras or other equipment that can capture photos or record videos; which may result in the violation of individual privacy. To tackle this problem in the USA, the Center of Democracy and Technology (CDT) instructed Federal Avi- ation Administration (FAA) to develop specific regulations to preserve privacy. For this purpose, Privacy by Design (PbD), which supports compensations for privacy violations, was introduced [156]. PbD regulations notably restrict privacy intrusion. Furthermore, some countries, e.g., China, do not have specialized privacy protection regulations for drones. This requires drones to be updated as quickly as feasible in order to design appropriate requirements for drones. Privacy is a valuable personal right, yet it is rarely given much consideration in China. In reality, drones are not prevalent in infringing on private rights. Drones having photographic features, for Micromachines 2022, 13, 1593 25 of 34 example, jeopardize the privacy of people who are caught unaware by such cam- eras [157]. Moreover, contact tracking tools can assist in containing the spread of the virus, but these tools also invade people’s privacy. Smartphone-based COVID-19 tracking applications can access individuals’ information and location. The wristband for tracking COVID-19 patients can also cause privacy issues. Access to such sensitive information should not be given to any unauthorized user. There must be proper agreement or guidelines for data collecting and sharing and keeping secrecy. Further- more, monitoring people for social distancing also raises privacy concerns. Aerial surveillance can damage an individual’s freedom and privacy. • Cyber Security: COVID-19 has raised cyber security threats besides jeopardizing the economy and healthcare sectors. This is occurring due to the reliance on people to work remotely during lockdowns, which makes them vulnerable to malicious threats, such as fake vaccine products, fake health insurance, malware in COVID-19 resources, and phishing emails. Cyber security concerns can also impact industrial automation, e-commerce, and online business sectors. Thus, it is essential to develop innovative cyber security tools to ensure robust, reliable, normal, and safe operations. • Telehealth: CVOID-19 has proven to be a game-changer for several economic and industrial sectors such as healthcare [158] and the mask industry. There is an expand- ing upsurge in healthcare systems, operations, and users. It is difficult to handle this sudden rise without reliable and efficient wireless connectivity. AI, AR/VR, robotics, and IoT technologies are envisioned to revolutionize the healthcare industry. These technologies will propose innovative strategies such as remote surgery. These technolo- gies can also substantially reduce travel costs as patients can easily access healthcare professionals online. Thus, telehealth can support patients who are unable to visit hospitals in person. • A More Connected World: The COVID-19 pandemic is envisaged to revolutionize inter-connected technologies across the globe. There is a notable rise in the use of several applications for online conferencing, audio/video calls, and online classes. These applications, including Skype, Zoom, DingTalk, Tencent Meeting, and several other tools, are being used for e-learning and teleconferencing. People have become highly dependent on these tools, which can lead to a remote workforce in the future. Furthermore, the integration of other promising technologies such as 5G/6G, IoT, and ML/DL will pave a way for virtual conferencing, classrooms, hospitals, and labs. • The IoT Revolution: During COVID-19, there has been a notable increase in IoT networks, consequently leading to the automation of various sectors such as trans- portation, consumer electronics, manufacturing, and health. With the integration of 5G and future 6G technologies, IoT is expected to transform the current world into a smart world of smart cities, smart oceans, smart hospitals, smart factories, smart spaces, etc. The ongoing research activities in this domain can help the research and medical communities to adopt innovative strategies to handle any pandemic or calamity in the future. • While making decisions about counter-drone technologies, security professionals, law enforcement agencies, technology developers, and government officials mostly consider two factors: cost and efficiency. The vast societal impacts are mostly dis- regarded. Ignoring economic aspects can lead to market loss. Ignoring efficiency can lead to users’ bad response and less trust in adopting this technology, which can ultimately decrease market value. It can lead to psychological stress for both users and developers [159]. A recent study discusses the emotional evaluation of drone safety and perception of risk during the COVID-19 outbreak in Italy [160]. It is very essential to estimate safety and risk for security and safety management. For this purpose, the use of opinion mining and sentiment analysis to ensure public security and safety has attracted remarkable interest. • Reliability: Because of erratic topological changes, UAV networks might encounter malicious nodes or a bottleneck relationship between two source destination sets. A Micromachines 2022, 13, 1593 26 of 34 standard approach for ensuring reliability is to provide an alternative channel between a particular destination and a given source via a group of UAV relays. Nevertheless, the great mobility and short battery lifespan of UAVs make such a system ineffective [37]. • Artificial intelligence faces an obstacle concerning human–machine interaction. This domain is at the crossroads of several AI disciplines and must identify them holisti- cally: modeling humans and human cognition; procuring, portraying, and exploiting abstract knowledge at the personal level in an amenable fashion; explanation of this information needed to make decisions; and ultimately instantiating those choices into physical actions both legible to and in cooperation with humans. Human–computer interaction (HCI) experts endeavor to transform breakthrough technologies such as quantum computing and AI into attractive, user-friendly human–machine interaction interfaces that are pertinent to our daily lives [161]. Natural language processing (NLP) is an essential approach for increasing the comprehension of human–machine interactions. The credibility of designed systems and their acceptance by human users are major concerns for AI professionals. Additionally, they exhibit certain patterns that demonstrate how this structure supports the administration and operation of associated learning pathways and model training while bringing verified AI laws. The surveillance and information accumulation unit, the trust computation and assessment unit, the trust recommendation unit, the decision-making unit, and the diffusion of the detection unit are the five basic parts of trust-based strategies. Currently, a trust-based approach creates a reputation system made up of many parts to offer security from route assaults. Within the trust monitoring/development process, it is possible to identify abnormalities and misbehavior of drones in the system. For a job stated in the swarm, offline/online collision assessment/detection and stochastic models that evaluate collisions can be employed. Unfortunately, the edge analytic capacities are limited by end-to-end latency requirements and the severe limits of the swarm drones’ systems. Verified scalable holistic abstraction can reduce latency in bottlenecks by automating the life cycle of AI interactions. 7. Conclusions During the COVID-19 pandemic crisis, healthcare practitioners and researchers have investigated technological approaches for combating the virus. Researchers from a number of disciplines presented a variety of techniques and possibilities to combat this epidemic. In this light, drones have shown to be a useful instrument in the battle against COVID- 19 and have demonstrated tremendous promise by delivering speedy services. Drone technology may become one of the best friends in the fight against COVID-19-like diseases in a future of enforced social isolation. This research investigates drone technology via the use of real-world examples and applicable application situations. Drones may be used with several modern technologies to allow dispersed processing features. This paper also discusses briefly a number of challenges and opportunities notwithstanding this fact. Several use cases of drones for reducing the risk of COVID-19 are discussed in this study, including surveillance, inspection, message broadcasting through QR codes and loudspeakers, medical supply delivery, disinfectant spraying, patient screening, and patient identification. In addition, the authors have discussed a number of emerging technologies, including artificial intelligence, machine learning, deep learning, blockchain, the Internet of Things, edge computing, and virtual reality, which offer a variety of technical innovations to manage this disease with minimal human intervention. The authors finished this research with the hope that it would help academics and professionals overcome the many obstacles that make it hard to use UAVs in these kinds of situations. Author Contributions: Conceptualization, S.A.H.M. and Q.u.A.Z.; methodology, S.A.H.M. and M.A.K.; resources, S.A.H.M., M.H.A., I.A.E. and A.J.; writing—original draft preparation, S.A.H.M., M.A.K. and Q.u.A.Z.; writing—review and editing, M.H.A. and I.A.E.; supervision, M.H.A. and A.J.; funding acquisition, M.H.A. All authors have read and agreed to the published version of the manuscript. Micromachines 2022, 13, 1593 27 of 34 Funding: This research received no external funding. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. Abbreviation Label Explanation 5G Fifth Generation B5G Beyond Fifth Generation 6G Sixth Generation AI Artificial Intelligence ANN Artificial Neural Network AP Access Point AI Artificial Intelligence AR Augmented Reality BC Blockchain BS Base Station CNN Convolutional Neural Network DoS Denial of Service DTL Deep Transfer Learning DL Deep Learning DNN Deep Neural Network FAA Federal Aviation Administration FANET Flying Ad Hoc Network GCS Ground Control Station GPS Global Positioning System HCI Human–Computer Interaction HMDs Head-Mounted Displays IDS Intrusion Detection System IoT Internet of Things IoMT Internet of Medical Things LoS Line of Sight LPT Laser Power Transfer LTE Long-Term Evolution MBS Macro Base Station MEC Mobile Edge Computing MIMO Multiple-Input Multiple-Output ML Machine Learning NFV Network Function Virtualization NLP Natural Language Processing QoS Quality of Service QML Quantum Machine Learning PbD Privacy by Design PoW Proof of Work PPE Personal protective equipment RAN Radio Access Network RF Radio Frequency RIS Reconfigurable Intelligent Surface SBS Small Base Station SDN Software-defined Networking SMS Social Monitoring System THz Terahertz UAV Unmanned Aerial Vehicle uHDD Ultra-High Data Density uHSLLC Ultra-High Speed Low Latency Communications uMUB Ubiquitous Mobile Ultrabroadband Micromachines 2022, 13, 1593 28 of 34 URLLC Ultra-Reliable Low-Latency Communications VR Virtual Reality VLC Visible-Light Communications XR Extended Reality WCA Water Cycle Algorithm WPT Wireless Power Transfer WHO World Health Organization References 1. Naren, N.; Chamola, V.; Baitragunta, S.; Chintanpalli, A.; Mishra, P.; Yenuganti, S.; Guizani, M. IoMT and DNN-enabled drone-assisted covid-19 screening and detection framework for rural areas. IEEE Internet Things Mag. 2021, 4, 4–9. [CrossRef] 2. Murphy, R.R.; Gandudi, V.B.M.; Adams, J. Applications of robots for COVID-19 response. arXiv 2020, arXiv:2008.06976. 3. Zhao, Z.; Ma, Y.; Mushtaq, A.; Rajper, A.M.A.; Shehab, M.; Heybourne, A.; Tse, Z.T.H. Applications of robotics, artificial intelligence, and digital technologies during COVID-19: A review. Disaster Med. Public Health Prep. 2020, 16, 1634–1644. [CrossRef] [PubMed] 4. Restás, Á. Drone Applications Fighting COVID-19 Pandemic—Towards Good Practices. Drones 2022, 6, 15. [CrossRef] 5. Piccialli, F.; Di Cola, V.S.; Giampaolo, F.; Cuomo, S. The role of artificial intelligence in fighting the COVID-19 pandemic. Inf. Syst. Front. 2021, 23, 1467–1497. [CrossRef] [PubMed] 6. Saeed, N.; Bader, A.; Al-Naffouri, T.Y.; Alouini, M.S. When wireless communication responds to COVID-19: Combating the pandemic and saving the economy. Front. Commun. Netw. 2020, 1, 566853. [CrossRef] 7. Sumartojo, S.; Lugli, D. Lively robots: Robotic technologies in COVID-19. Soc. Cult. Geogr. 2021, 1–18. [CrossRef] 8. Chen, B.; Marvin, S.; While, A. Containing COVID-19 in China: AI and the robotic restructuring of future cities. Dialogues Hum. Geogr. 2020, 10, 238–241. [CrossRef] 9. Kumar, A.; Sharma, K.; Singh, H.; Naugriya, S.G.; Gill, S.S.; Buyya, R. A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic. Future Gener. Comput. Syst. 2021, 115, 1–19. [CrossRef] 10. Drone Technology: A New Ally in the Fight Against COVID-19. Available online: https://www.mdlinx.com/internal-medicine/ article/6767 (accessed on 15 April 2022). 11. Draganfly Selected to Globally Integrate Breakthrough Health Diagnosis Technology Immediately onto Autonomous Camera’s and Specialized Drones to Combat Coronavirus (COVID-19) and Future Health Emergencies. Available online: https://apnews. com/Globe%20Newswire/dc01344350423d7d64c99ebbe8fb7548 (accessed on 20 June 2021). 12. WHO. Declaration of Public Health Emergency of International Concern. Available online: https://www.who.int/emergencies/ diseases/novel-coronavirus-2019/events-as-they-happen (accessed on 27 June 2020). 13. STAT. Surge in Patients Overwhelms Telehealth Services Amid Coronavirus Pandemic. Available online: https://www.statnews. com/2020/03/17/telehealth-services-overwhelmed-amid-coronavirus-pandemic/ (accessed on 25 June 2021). 14. Alsuliman, T.; Humaidan, D.; Sliman, L. Machine learning and artificial intelligence in the service of medicine: Necessity or potentiality? Curr. Res. Transl. Med. 2020, 68, 245–251. [CrossRef] 15. Graboyes, R.F.; Skorup, B. Medical drones in the United States and a survey of technical and policy challenges. Mercatus Cent. Policy Brief 2020. [CrossRef] 16. Arafat, M.Y.; Moh, S. Location-aided delay tolerant routing protocol in UAV networks for post-disaster operation. IEEE Access 2018, 6, 59891–59906. [CrossRef] 17. Ullah, H.; Nair, N.G.; Moore, A.; Nugent, C.; Muschamp, P.; Cuevas, M. 5G communication: An overview of vehicle-to-everything, drones, and healthcare use-cases. IEEE Access 2019, 7, 37251–37268. [CrossRef] 18. Jones, R.W.; Despotou, G. Unmanned aerial systems and healthcare: Possibilities and challenges. In Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi’an, China, 19–21 June 2019; pp. 189–194. 19. Yakushiji, K.; Fujita, H.; Murata, M.; Hiroi, N.; Hamabe, Y.; Yakushiji, F. Short-range transportation using unmanned aerial vehicles (UAVs) during disasters in Japan. Drones 2020, 4, 68. [CrossRef] 20. Shao, Z.; Cheng, G.; Ma, J.; Wang, Z.; Wang, J.; Li, D. Real-time and accurate UAV pedestrian detection for social distancing monitoring in COVID-19 pandemic. IEEE Trans. Multimed. 2021, 24, 2069–2083. [CrossRef] [PubMed] 21. Lv, Z.; Chen, D.; Feng, H.; Zhu, H.; Lv, H. Digital twins in unmanned aerial vehicles for rapid medical resource delivery in epidemics. IEEE Trans. Intell. Transp. Syst. 2021, 1–9. [CrossRef] 22. Rezaee, K.; Zadeh, H.G.; Chakraborty, C.; Khosravi, M.R.; Jeon, G. Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning. IEEE Trans. Ind. Inform. 2022. [CrossRef] 23. Aslan, M.F.; Hasikin, K.; Yusefi, A.; Durdu, A.; Sabanci, K.; & Azizan, M.M. COVID-19 Isolation Control Proposal via UAV and UGV for Crowded Indoor Environments: Assistive Robots in the Shopping Malls. Front. Public Health 2022, 10, 855994. [CrossRef] 24. Khan, I.H.; Javaid, M. Automated COVID-19 emergency response using modern technologies. Apollo Med. 2020, 17, 58. 25. Luo, H.; Tang, Q.L.; Shang, Y.X.; Liang, S.B.; Yang, M.; Robinson, N.; Liu, J.P. Can Chinese medicine be used for prevention of corona virus disease 2019 (COVID-19)? A review of historical classics, research evidence and current prevention programs. Chin. J. Integr. Med. 2020, 26, 243–250. [CrossRef] Micromachines 2022, 13, 1593 29 of 34 26. Cinelli, M.; Quattrociocchi, W.; Galeazzi, A.; Valensise, C.M.; Brugnoli, E.; Schmidt, A.L.; Scala, A. The COVID-19 social media infodemic. Sci. Rep. 2020, 10, 16598. [CrossRef] [PubMed] 27. Klemeš, J.J.; Van Fan, Y.; Jiang, P. COVID-19 pandemic facilitating energy transition opportunities. Int. J. Energy Res. 2020, 45, 3457–3463. [CrossRef] [PubMed] 28. Mohsan, S.A.H.; Khan, M.A.; Noor, F.; Ullah, I.; Alsharif, M.H. Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones 2022, 6, 147. [CrossRef] 29. Tian, H.; Huang, N.; Niu, Z.; Qin, Y.; Pei, J.; Wang, J. Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm. Remote Sens. 2019, 11, 820. [CrossRef] 30. Tian, H.; Pei, J.; Huang, J.; Li, X.; Wang, J.; Zhou, B.; Wang, L. Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China. Remote Sens. 2020, 12, 3539. [CrossRef] 31. Mohsan, S.A.H.; Othman, N.Q.H.; Khan, M.A.; Amjad, H.; Żywiołek, J. A Comprehensive Review of Micro UAV Charging Techniques. Micromachines 2022, 13, 977. [CrossRef] [PubMed] 32. Yaacoub, J.P.; Noura, H.; Salman, O.; Chehab, A. Security analysis of drones systems: Attacks, limitations, and recommendations. Internet Things 2020, 11, 100218. [CrossRef] 33. Butt, U.J.; Richardson, W.; Abbod, M.; Agbo, H.M.; Eghan, C. The deployment of autonomous drones during the COVID-19 pandemic. In Cybersecurity, Privacy and Freedom Protection in the Connected World; Springer: Cham, Switzerland, 2021; pp. 183–220. 34. Altawy, R.; Youssef, A.M. Security, privacy, and safety aspects of civilian drones: A survey. ACM Trans. Cyber-Phys. Syst. 2016, 1, 1–25. [CrossRef] 35. Chamola, V.; Kotesh, P.; Agarwal, A.; Gupta, N.; Guizani, M. A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques. Ad Hoc Netw. 2021, 111, 102324. [CrossRef] 36. Hu, T.; Wang, S.; She, B.; Zhang, M.; Huang, X.; Cui, Y.; Li, Z. Human Mobility Data in the COVID-19 Pandemic: Characteristics, Applications, and Challenges. Int. J. Digit. Earth 2021, 14, 1126–1147. [CrossRef] 37. Abouzaid, L.; Elbiaze, H.; Sabir, E. Agile roadmap for application-driven Multi-UAV networks: The case of COVID-19. IET Netw. 2022. [CrossRef] 38. Kazakhstan Uses Drones to Patrol Capital City During COVID-19 Lockdown. Available online: https://www.terra-drone.net/ global/2020/04/09/kazakhstan-drones-patrol-coronavirus-covid-19-lockdown/ (accessed on 25 June 2021). 39. In Fight against Coronavirus, Governments Embrace Surveillance. Available online: https://www.politico.eu/article/ coroanvirus-covid19-surveillance-data/ (accessed on 25 June 2022). 40. Drones Will Check Whether People Are COVID-19 Social Distancing at Sydney’s Northern Beaches. Available online: https: //www.abc.net.au/news/2020-12-28/covid-19-distancing-enforced-by-drones-at-some-sydney-beaches/12981600 (accessed on 25 June 2022). 41. Drones Are Enabling Authorities to Implement an Effective COVID-19 Lockdown. Available online: https://www.cyient.com/ blog/drones-are-enabling-authorities-to-implement-an-effective-covid-19-lockdown (accessed on 25 June 2022). 42. Spain’s Police Are Flying Drones with Speakers around Public Places to Warn Citizens on Coronavirus Lockdown to Get Inside. Available online: https://www.businessinsider.com/spanish-police-using-drones-to-ask-people-stay-at-home-2020-3?r=US& IR=T (accessed on 25 June 2022). 43. Use of Drones to Battle COVID-19 Is Troubling. Available online: https://www.nj.com/opinion/2020/05/use-of-drones-to- battle-covid-19-is-troubling.html (accessed on 25 June 2022). 44. Police Are Using Drones to Yell at People for Being Outside. Available online: https://www.popularmechanics.com/technology/ a31708776/police-drones-social-distancing-coronavirus/ (accessed on 25 June 2022). 45. ‘Yes, This Drone Is Speaking to You’: How China Is Reportedly Enforcing Coronavirus Rules. Available online: https:// globalnews.ca/news/6535353/china-coronavirus-drones-quarantine/ (accessed on 25 June 2022). 46. Malaysian Authorities Use Drones to Curb Spread of COVID-19. Available online: https://www.airmedandrescue.com/latest/ news/malaysian-authorities-use-drones-curb-spread-covid-19 (accessed on 25 June 2022). 47. Police Agencies Are Using Drones to Enforce Stay-At-Home Orders, Raising Concerns among Civil Rights Groups. Available online: https://www.usatoday.com/story/news/politics/2020/05/03/coronavirus-police-use-drones-enforcement-privacy- concerns/3059073001/ (accessed on 25 June 2022). 48. Drones Used in Effort to Slow the Spread of COVID-19. Available online: https://www.cbsnews.com/news/coronavirus-drones- slow-spread-covid-19/ (accessed on 28 June 2022). 49. Rwandan Drones Take to Air with COVID-19 Messages. Available online: https://adf-magazine.com/2020/09/rwandan-drones- take-to-air-with-covid-19-messages/ (accessed on 28 June 2022). 50. Coronavirus Crisis: Police Drones to Spy on Self-Isolation Rule Breakers. Available online: https://www.perthnow.com.au/news/ coronavirus/coronavirus-crisis-police-drones-to-spy-on-self-isolation-rule-breakers-ng-b881504581z (accessed on 20 May 2020). 51. China Takes Various Measures to Combat Epidemic. Available online: https://www.globaltimes.cn/page/202002/1179287.shtml (accessed on 25 June 2022). 52. 300 Drones Illuminate the Sky to Honor Health Care Workers. Available online: https://www.wired.com/story/drones-covid- 19-health-care-workers-netherlands/ (accessed on 20 July 2022). 53. Spectacular Drone Displays Reminds South Koreans of COVID-19 Precautions. Available online: https://www.youtube.com/ watch?v=EN7zKk1w2L0 (accessed on 25 June 2022). Micromachines 2022, 13, 1593 30 of 34 54. Restás, Á.; Szalkai, I.; Óvári, G. Drone Application for Spraying Disinfection Liquid Fighting against the COVID-19 Pandemic— Examining Drone-Related Parameters Influencing Effectiveness. Drones 2021, 5, 58. [CrossRef] 55. Alsamhi, S.H.; Lee, B.; Guizani, M.; Kumar, N.; Qiao, Y.; Liu, X. Blockchain for decentralized multi-drone to combat COVID-19 and future pandemics: Framework and proposed solutions. Trans. Emerg. Telecommun. Technol. 2021, 32, e4255. [CrossRef] 56. González Jorge, H.; González de Santos, L.M.; Fariñas Álvarez, N.; Martínez Sánchez, J.; Navarro Medina, F. Operational study of drone spraying application for the disinfection of surfaces against the COVID-19 pandemic. Drones 2021, 5, 18. [CrossRef] 57. 3 Ways China Is Using Drones to Fight Coronavirus. Available online: https://www.weforum.org/agenda/2020/03/three-ways- china-is-using-drones-to-fight-coronavirus/ (accessed on 25 June 2022). 58. Dubai Municipality Continues Campaign to Disinfect Public Facilities. Available online: https://gulfnews.com/uae/dubai- municipality-continues-campaign-to-disinfect-public-facilities-1.1585420891506 (accessed on 25 June 2022). 59. Spain’s Military Uses DJI Agricultural Drones to Spray Disinfectant in Fight Against COVID-19. Available online: https:// www.scmp.com/tech/gear/article/3077945/spains-military-uses-dji-agricultural-drones-spray-disinfectant-fight (accessed on 25 June 2022). 60. Disinfecting Drones Launching to Fight COVID-19. Available online: https://abc7ny.com/disinfecting-drones-coronavirus-new- york-ny-cases-in/6176157/ (accessed on 25 June 2022). 61. Coronavirus Prevention: Dubai Uses Drones to Sterilise Streets. Available online: https://www.youtube.com/watch?v=eP40sS5 EcLU (accessed on 25 June 2022). 62. Mohammed, M.N.; Syamsudin, H.; Hazairin, N.A.; Haki, M.; Al-Zubaidi, S.; Sairah, A.K.; Eddy, Y. Toward a novel design for spray disinfection system to combat coronavirus (COVID-19) using IoT based drone technology. Rev. Argent. De Clínica Psicológica 2020, 29, 240. 63. A Role of Drones in Healthcare. Available online: https://www.dronesinhealthcare.com/ (accessed on 25 June 2022). 64. Sham, R.; Siau, C.S.; Tan, S.; Kiu, D.C.; Sabhi, H.; Thew, H.Z.; Ramli, M.H.M. Drone Usage for Medicine and Vaccine Delivery during the COVID-19 Pandemic: Attitude of Health Care Workers in Rural Medical Centres. Drones 2022, 6, 109. [CrossRef] 65. Comtet, H.E.; Johannessen, K.A. The moderating role of pro-innovative leadership and gender as an enabler for future drone transports in healthcare systems. Int. J. Environ. Res. Public Health 2021, 18, 2637. [CrossRef] 66. Panwar, A.; Sharma, N.; Garg, K.; Bahurupi, Y.; Singh, M.; Aggarwal, P. Mobile Vaccine Vans and Drones-the Future of Vaccination Delivery System. J. Compr. Health 2021, 9, 41–43. [CrossRef] 67. Sun, X.; Andoh, E.A.; Yu, H. A simulation-based analysis for effective distribution of COVID-19 vaccines: A case study in Norway. Transp. Res. Interdiscip. Perspect. 2021, 11, 100453. [CrossRef] 68. Kim, J.J.; Kim, I.; Hwang, J. A change of perceived innovativeness for contactless food delivery services using drones after the outbreak of COVID-19. Int. J. Hosp. Manag. 2021, 93, 102758. [CrossRef] 69. Flemons, K.; Baylis, B.; Khan, A.Z.; Kirkpatrick, A.W.; Whitehead, K.; Moeini, S.; Hawkins, W. The use of drones for the delivery of diagnostic test kits and medical supplies to remote First Nations communities during COVID-19. Am. J. Infect. Control. 2022, 50, 849–856. [CrossRef] [PubMed] 70. KAYA, Y.Ç.; Mahir, K.A.Y.A.; AKDAĞ, A. Route Optimization for Medication Delivery of COVID-19 Patients with Drones. Gazi Univ. J. Sci. Part C Des. Technol. 2021, 9, 478–491. 71. Drones: A Cost-Effective Solution to Boost Nepal’s Immunization Efforts in Hard-to-Reach Areas. Available online: https://www. unicef.org/supply/stories/drones-cost-effective-solution-boost-nepals-immunization-efforts-hard-reach-areas (accessed on 25 June 2022). 72. Assessing the Impact of Drones in the Global COVID Response. Available online: https://www.brookings.edu/techstream/ assessing-the-impact-of-drones-in-the-global-covid-response/ (accessed on 25 June 2022). 73. Haidari, L.A.; Brown, S.T.; Ferguson, M.; Bancroft, E.; Spiker, M.; Wilcox, A.; Lee, B.Y. The economic and operational value of using drones to transport vaccines. Vaccine 2016, 34, 4062–4067. [CrossRef] 74. Hwang, J.; Kim, D.; Kim, J.J. How to form behavioral intentions in the field of drone food delivery services: The moderating role of the COVID-19 outbreak. Int. J. Environ. Res. Public Health 2020, 17, 9117. [CrossRef] [PubMed] 75. Available online: https://www.nature.com/articles/d41591-022-00053-9 (accessed on 25 June 2022). 76. Lamptey, E.; Serwaa, D. The use of zipline drones technology for COVID-19 samples transportation in Ghana. HighTech Innov. J. 2020, 1, 67–71. [CrossRef] 77. UPS Operates First Ever U.S. Drone COVID-19 Vaccine Delivery. Available online: https://about.ups.com/us/en/our-stories/ innovation-driven/drone-covid-vaccine-deliveries.html (accessed on 25 June 2022). 78. NHS Using Drones to Deliver Coronavirus Kit between Hospitals. Available online: https://www.theguardian.com/technology/ 2020/oct/17/nhs-drones-deliver-coronavirus-kit-between-hospitals-essex (accessed on 25 June 2022). 79. In Indonesia, Drone Deliveries Provide Lifeline for Isolating COVID Patients. Available online: https://www.reuters.com/ world/asia-pacific/indonesia-drone-deliveries-provide-lifeline-isolating-covid-patients-2021-09-01/ (accessed on 25 June 2022). 80. Drones Take to Scottish Skies to Support COVID Battle. Available online: https://business.esa.int/news/drones-take-to-scottish- skies-to-support-covid-battle (accessed on 25 June 2022). 81. COVID-19: Shanghai Firefighters Use Drones to Deliver Medicine to People in Lockdown. Available online: https://news. sky.com/video/covid-19-shanghai-firefighters-use-drones-to-deliver-medicine-to-people-in-lockdown-12589016 (accessed on 25 June 2022). Micromachines 2022, 13, 1593 31 of 34 82. Walmart Now Piloting Drone Delivery of COVID-19 At-Home Self-Collection Kits. Available online: https://corporate. walmart.com/newsroom/2020/09/22/walmart-now-piloting-drone-delivery-of-covid-19-at-home-self-collection-kits (accessed on 28 June 2022). 83. Khan, H.; Kushwah, K.K.; Singh, S.; Urkude, H.; Maurya, M.R.; Sadasivuni, K.K. Smart technologies driven approaches to tackle COVID-19 pandemic: A review. 3 Biotech 2021, 11, 50. [CrossRef] 84. Khan, M.A.; Ullah, I.; Alsharif, M.H.; Alghtani, A.H.; Aly, A.A.; Chen, C.M. An Efficient Certificate-Based Aggregate Signature Scheme for Internet of Drones. Secur. Commun. Netw. 2022, 2022, 9718580. [CrossRef] 85. Gupta, R.; Kumari, A.; Tanwar, S.; Kumar, N. Blockchain-envisioned softwarized multi-swarming UAVs to tackle COVID-I9 situations. IEEE Netw. 2020, 35, 160–167. [CrossRef] 86. Conte, C.; De Alteriis, G.; De Pandi, F.; Caputo, E.; Moriello, R.S.L.; Rufino, G.; Accardo, D. Performance analysis for human crowd monitoring to control covid-19 disease by drone surveillance. In Proceedings of the 2021 IEEE 8th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Naples, Italy, 23–25 June 2021; pp. 31–36. 87. UniSA Working on ‘Pandemic Drone’ to Detect Coronavirus. Available online: https://www.unisa.edu.au/unisanews/2020 /autumn/story11/ (accessed on 25 June 2022). 88. COVID-19: IIT Alumni Develop Drone with Infrared Camera for Thermal Screening. Available online: https://www. deccanherald.com/national/covid-19-iit-alumni-develop-drone-with-infrared-camera-for-thermal-screening-820801.html (accessed on 25 June 2022). 89. How Drones Are Being Used to Combat COVID-19. Available online: https://www.geospatialworld.net/blogs/how-drones-are- being-used-to-combat-covid-19/ (accessed on 25 June 2022). 90. Draganfly’s ‘Pandemic Drone’ technology Conducts Initial Flights Near New York City to Detect COVID-19 Symptoms and Identify Social Distancing. Available online: https://www.globenewswire.com/news-release/2020/04/21/2019221/0/en/ Draganfly-s-Pandemic-Drone-technology-Conducts-Initial-Flights-Near-New-York-City-to-Detect-COVID-19-Symptoms- and-Identify-Social-Distancing.html (accessed on 25 June 2022). 91. Peng, Y.; Liu, E.; Peng, S.; Chen, Q.; Li, D.; Lian, D. Using artificial intelligence technology to fight COVID-19: A review. Artif. Intell. Rev. 2022, 55, 4941–4977. [CrossRef] 92. Nasajpour, M.; Pouriyeh, S.; Parizi, R.M.; Dorodchi, M.; Valero, M.; Arabnia, H.R. Internet of Things for current COVID-19 and future pandemics: An exploratory study. J. Healthc. Inform. Res. 2020, 4, 325–364. [CrossRef] 93. Bai, L.; Yang, D.; Wang, X.; Tong, L.; Zhu, X.; Zhong, N.; Tan, F. Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19). Clin. Ehealth 2020, 3, 7–15. [CrossRef] 94. COVID-19 Smartphone Testing Kit. Available online: https://www.detectachem.com/index.php?p=COVID19 (accessed on 25 June 2022). 95. United Against Coronavirus! Stopcorona App. Available online: https://stopcorona.app/ (accessed on 6 June 2020). 96. Kelion, L. Coronavirus: Moscow Rolls Out Patient-Tracking App. Available online: https://www.bbc.com/news/technology-52 121264 (accessed on 19 June 2020). 97. ‘Selfie App’ to Keep Track of Quarantined Poles. Available online: https://www.france24.com/en/20200320-selfie-app-to-keep- track-of-quarantined-poles (accessed on 25 June 2022). 98. Hui, M. Hong Kong Is Using Tracker Wristbands to Geofence People under Coronavirus Quarantine. Available online: https://qz.com/ 1822215/hong-kong-uses-tracking-wristbands-for-coronavirus-quarantine/ (accessed on 25 June 2022). 99. Wright, T. Blockchain App Used to Track COVID-19 Cases in Latin America. Available online: https://cointelegraph.com/news/ blockchain-app-used-to-track-covid-19-cases-in-latin-america (accessed on 25 June 2022). 100. TraceTogether, Safer Together. Available online: https://www.tracetogether.gov.sg/ (accessed on 25 June 2022). 101. Berglund, M.E.; Duvall, J.; Dunne, L.E. A survey of the historical scope and current trends of wearable technology applications. In Proceedings of the 2016 ACM International Symposium on Wearable Computers, Heidelberg, Germany, 12–16 September 2016; pp. 40–43. 102. Singh, V.; Chandna, H.; Kumar, A.; Kumar, S.; Upadhyay, N.; Utkarsh, K. IoT-Q-Band: A low cost internet of things based wearable band to detect and track absconding COVID-19 quarantine subjects. EAI Endorsed Trans. Internet Things 2020, 6, e5. [CrossRef] 103. Martínez-Galdámez, M.; Fernández, J.G.; Arteaga, M.S.; Pérez-Sánchez, L.; Arenillas, J.F.; Rodríguez-Arias, C.; Kalousek, V. Smart glasses evaluation during the COVID-19 pandemic: First-use on Neurointerventional procedures. Clin. Neurol. Neurosurg. 2021, 205, 106655. [CrossRef] [PubMed] 104. Mohammed, M.N.; Syamsudin, H.; Al-Zubaidi, S.; AKS, R.R.; Yusuf, E. Novel COVID-19 detection and diagnosis system using IOT based smart helmet. Int. J. Psychosoc. Rehabil. 2020, 24, 2296–2303. 105. Awotunde, J.B.; Jimoh, R.G.; AbdulRaheem, M.; Oladipo, I.D.; Folorunso, S.O.; Ajamu, G.J. IoT-based wearable body sensor network for COVID-19 pandemic. In Advances in Data Science and Intelligent Data Communication Technologies for COVID-19; Springer: Cham, Switzerland, 2022; pp. 253–275. 106. Tripathy, A.K.; Mohapatra, A.G.; Mohanty, S.P.; Kougianos, E.; Joshi, A.M.; Das, G. EasyBand: A wearable for safety-aware mobility during pandemic outbreak. IEEE Consum. Electron. Mag. 2020, 9, 57–61. [CrossRef] 107. Mohammed, M.; Hazairin, N.A.; Syamsudin, H.; Al-Zubaidi, S.; Sairah, A.K.; Mustapha, S.; Yusuf, E. 2019 novel coronavirus disease (COVID-19): Detection and diagnosis system using IoT based smartglasses. Int. J. Adv. Sci. Technol. 2020, 29, 954–960. Micromachines 2022, 13, 1593 32 of 34 108. Chamberlain, S.D.; Singh, I.; Ariza, C.; Daitch, A.; Philips, P.; Dalziel, B.D. Real-time detection of COVID-19 epicenters within the United States using a network of smart thermometers. MedRxiv 2020. [CrossRef] 109. Puri, V.; Kataria, A.; Sharma, V. Artificial intelligence-powered decentralized framework for Internet of Things in Healthcare 4.0. Trans. Emerg. Telecommun. Technol. 2021, e4245. [CrossRef] 110. Xenex. Martin Health System Unveils Xenex Germ-Zapping Robot. Available online: https://www.xenex.com/resources/news/ martin-health-system-unveils-xenex-germ-zapping-robot/ (accessed on 25 June 2022). 111. Ramadass, L.; Arunachalam, S.; Sagayasree, Z. Applying deep learning algorithm to maintain social distance in public place through drone technology. Int. J. Pervasive Comput. Commun. 2020, 16, 223–234. [CrossRef] 112. Bhattacharya, S.; Maddikunta, P.K.R.; Pham, Q.V.; Gadekallu, T.R.; Chowdhary, C.L.; Alazab, M.; Piran, M.J. Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustain. Cities Soc. 2021, 65, 102589. [CrossRef] 113. Othman, N.A.; Aydin, I. A Low-Cost Embedded Security System for UAV-Based Face Mask Detector Using IoT and Deep Learning to Reduce COVID-19. In Proceedings of the 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, 23–25 March 2022; pp. 693–697. 114. Aabid, A.; Parveez, B.; Parveen, N.; Khan, S.A.; Shabbir, O. A Case Study of Unmanned Aerial Vehicle (Drone) Technology and Its Applications in the COVID-19 Pandemic. J. Mech. Eng. Res. Dev. 2022, 45, 70–77. 115. Smits, M.; Staal, J.B.; Van Goor, H. Could Virtual Reality play a role in the rehabilitation after COVID-19 infection? BMJ Open Sport Exerc. Med. 2020, 6, e000943. [CrossRef] [PubMed] 116. Mohammed, M.; Hazairin, N.A.; Al-Zubaidi, S.; AK, S.; Mustapha, S.; Yusuf, E. Toward a novel design for coronavirus detection and diagnosis system using IoT based drone technology. Int. J. Psychosoc. Rehabil. 2020, 24, 2287–2295. 117. Huda, S.A.; Moh, S. Survey on computation offloading in UAV-Enabled mobile edge computing. J. Netw. Comput. Appl. 2022, 201, 103341. [CrossRef] 118. Kalra, R.; Sahni, V. COVID-based edge-drone application approach. In Innovations in Cyber Physical Systems; Springer: Singapore, 2021; pp. 169–179. 119. Khan, M.A.; Ullah, I.; Alkhalifah, A.; Rehman, S.U.; Shah, J.A.; Uddin, I.I.; Alsharif, M.H.; Algarni, F. A Provable and Privacy-Preserving Authentication Scheme for UAV-Enabled Intelligent Transportation Systems. IEEE Trans. Ind. Inform. 2022, 18, 3416–3425. [CrossRef] 120. Khan, A.U.; Javaid, N.; Khan, M.A.; Ullah, I. A blockchain scheme for authentication, data sharing and nonrepudiation to secure internet of wireless sensor things. Clust. Comput. 2022. [CrossRef] 121. Fraune, M.R.; Khalaf, A.S.; Zemedie, M.; Pianpak, P.; NaminiMianji, Z.; Alharthi, S.A.; Toups, Z.O. Developing future wearable interfaces for human-drone teams through a virtual drone search game. Int. J. Hum.-Comput. Stud. 2021, 147, 102573. [CrossRef] 122. Van, L.D.; Zhang, L.Y.; Chang, C.H.; Tong, K.L.; Wu, K.R.; Tseng, Y.C. Things in the air: Tagging wearable IoT information on drone videos. Discov. Internet Things 2021, 1, 6. [CrossRef] 123. Cao, Z.; Wang, Y.; Zheng, W.; Yin, L.; Tang, Y.; Miao, W.; Yang, B. The algorithm of stereo vision and shape from shading based on endoscope imaging. Biomed. Signal Processing Control. 2022, 76, 103658. [CrossRef] 124. Liu, S.; Yang, B.; Wang, W.; Tian, J.; Yin, L.; Zheng, W. 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation. Appl. Sci. 2022, 12, 2828. [CrossRef] 125. Liu, Y.; Tian, J.; Hu, R.; Yang, B.; Liu, S.; Yin, L.; Zheng, W. Improved Feature Point Pair Purification Algorithm Based on SIFT During Endoscope Image Stitching. Front. Neurorob. 2022. [CrossRef] [PubMed] 126. Abdel-Basset, M.; Chang, V.; Nabeeh, N.A. An intelligent framework using disruptive technologies for COVID-19 analysis. Technol. Forecast. Soc. Change 2021, 163, 120431. [CrossRef] [PubMed] 127. Will Social Distancing during the COVID-19 Crisis Drive Interest in Drones for Supply Chains? Available online: https://www. wipro.com/semiconductors/drones-for-supply-chains/ (accessed on 25 June 2022). 128. Ateya, A.A.; Algarni, A.D.; Koucheryavy, A.; Soliman, N.F. Drone-based AI/IoT Framework for Monitoring, Tracking and Fighting Pandemics. Comput. Mater. Contin. 2022, 71, 4677–4699. [CrossRef] 129. Almalki, F.A.; Alotaibi, A.A.; Angelides, M.C. Coupling multifunction drones with AI in the fight against the coronavirus pandemic. Computing 2022, 104, 1033–1059. [CrossRef] 130. De Ponti, R.; Marazzato, J.; Maresca, A.M.; Rovera, F.; Carcano, G.; Ferrario, M.M. Pre-graduation medical training including virtual reality during COVID-19 pandemic: A report on students’ perception. BMC Med. Educ. 2020, 20, 332. [CrossRef] [PubMed] 131. Matamala-Gomez, M.; Bottiroli, S.; Realdon, O.; Riva, G.; Galvagni, L.; Platz, T.; Tassorelli, C. Telemedicine and virtual reality at time of COVID-19 pandemic: An overview for future perspectives in neurorehabilitation. Front. Neurol. 2021, 12, 646902. [CrossRef] 132. Manto, M.; Dupre, N.; Hadjivassiliou, M.; Louis, E.D.; Mitoma, H.; Molinari, M.; Schmahmann, J.D. Management of patients with cerebellar ataxia during the COVID-19 pandemic: Current concerns and future implications. Cerebellum 2020, 19, 562–568. [CrossRef] 133. Drones Detecting Body Temperature Being Used in COVID-19 Response. Available online: https://www.kiro7.com/news/ local/drones-detecting-body-temperature-being-used-covid-19-response/CAGP3UM2IRCI7HMXPZMOL7OOXY/ (accessed on 29 June 2022). 134. Zhang, Z.; Ma, P.; Ahmed, R.; Wang, J.; Akin, D.; Soto, F.; Demirci, U. Advanced Point-of-Care Testing Technologies for Human Acute Respiratory Virus Detection. Adv. Mater. 2021, 2103646. [CrossRef] Micromachines 2022, 13, 1593 33 of 34 135. Singh, R.P.; Javaid, M.; Kataria, R.; Tyagi, M.; Haleem, A.; Suman, R. Significant applications of virtual reality for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clin. Res. Rev. 2020, 14, 661–664. 136. Angurala, M.; Bala, M.; Bamber, S.S.; Kaur, R.; Singh, P. An internet of things assisted drone based approach to reduce rapid spread of COVID-19. J. Saf. Sci. Resil. 2020, 1, 31–35. [CrossRef] 137. Verma, A.; Bhattacharya, P.; Zuhair, M.; Tanwar, S.; Kumar, N. Vacochain: Blockchain-based 5g-assisted uav vaccine distribution scheme for future pandemics. IEEE J. Biomed. Health Inform. 2021, 26, 1997–2007. [CrossRef] [PubMed] 138. Zuhair, M.; Patel, F.; Navapara, D.; Bhattacharya, P.; Saraswat, D. BloCoV6: A blockchain-based 6G-assisted UAV contact tracing scheme for COVID-19 pandemic. In Proceedings of the 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, UK, 28–30 April 2021; pp. 271–276. 139. Mohanty, M.D.; Mallick, P.K.; Mohanty, M.N. Design of an Intelligent Controller of a Self-Tuned Quadcopter for IoT-Based Application on COVID Infected Area Sanitization. IEEE Internet Things Mag. 2021, 4, 24–29. [CrossRef] 140. Islam, A.; Rahim, T.; Masuduzzaman, M.D.; Shin, S.Y. A blockchain-based artificial intelligence-empowered contagious pandemic situation supervision scheme using internet of drone things. IEEE Wirel. Commun. 2021, 28, 166–173. [CrossRef] 141. Bakour, I.; Bouchali, H.N.; Allali, S.; Lacheheb, H. Soft-CSRNet: Real-time Dilated Convolutional Neural Networks for Crowd Counting with Drones. In Proceedings of the 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-Being (IHSH), Boumerdes, Algeria, 9–10 February 2021; pp. 28–33. 142. Elsayed, E.K.; Alsayed, A.M.; Salama, O.M.; Alnour, A.M.; Mohammed, H.A. Deep learning for covid-19 facemask detection using autonomous drone based on IoT. In Proceedings of the 2020 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), Khartoum, Sudan, 26 February–1 March 2021; pp. 1–5. 143. Somaldo, P.; Ferdiansyah, F.A.; Jati, G.; Jatmiko, W. Developing smart COVID-19 social distancing surveillance drone using YOLO implemented in robot operating system simulation environment. In Proceedings of the 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC), Kuching, Malaysia, 1–3 December 2020; pp. 1–6. 144. Xing, Y.; Carlson, C.; Yuan, H. Optimize Path Planning for UAV COVID-19 Test Kits Delivery System by Hybrid Reinforcement Learning. In Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 26–29 January 2022; pp. 177–183. 145. Iqbal, M.; Zafar, N.A.; & Alkhammash, E.H. Formally Identifying COVID-19 Patients for Providing Medical Services using Drones. In Proceedings of the 2021 International Conference of Women in Data Science at Taif University (WiDSTaif), Taif, Saudi Arabia, 30–31 March 2021; pp. 1–6. 146. Verma, A.; Bhattacharya, P.; Saraswat, D.; Tanwar, S.; Kumar, N.; Sharma, R. SanJeeVni: Secure UAV-envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network. IEEE Sens. J. 2022. [CrossRef] 147. Masmoudi, N.; Jaafar, W.; Cherif, S.; Abderrazak, J.B.; Yanikomeroglu, H. UAV-Based Crowd Surveillance in Post COVID-19 Era. IEEE Access 2021, 9, 162276–162290. [CrossRef] 148. Dantas, A.J.; Jesus, L.D.; Ramos, A.C.B.; Hokama, P.; Mora-Camino, F.; Katarya, R.; Ouahada, K. Using UAV, IoMT and AI for monitoring and supplying of COVID-19 patients. In ITNG 2021 18th International Conference on Information Technology-New Generations; Springer: Cham, Switzerland, 2021; pp. 383–386. 149. Islam, S.M.; Grado, C.; Lubecke, V.; Lubecke, L.C. UAV radar sensing of respiratory variations for COVID-type disorders. In Proceedings of the 2020 IEEE Asia-Pacific Microwave Conference (APMC), Hong Kong, China, 8–11 December 2020; pp. 737–739. 150. Singh, M.; Aujla, G.S.; Bali, R.S.; Batth, R.S.; Singh, A.; Vashisht, S.; Jindal, A. CovaDel: A blockchain-enabled secure and QoS-aware drone delivery framework for COVID-like pandemics. Computing 2022, 104, 1589–1613. [CrossRef] 151. Alsarhan, A.; Almalkawi, I.T.; Kilani, Y. New COVID-19 Tracing Approach using Machine Learning and Drones Enabled Wireless Network. Int. J. Interact. Mob. Technol. 2021, 15. [CrossRef] 152. Zhi, Y.; Fu, Z.; Sun, X.; Yu, J. Security and privacy issues of UAV: A survey. Mob. Netw. Appl. 2020, 25, 95–101. [CrossRef] 153. Noor, F.; Khan, M.A.; Al-Zahrani, A.; Ullah, I.; Al-Dhlan, K.A. A Review on Communications Perspective of Flying Ad-Hoc Networks: Key Enabling Wireless Technologies, Applications, Challenges and Open Research Topics. Drones 2020, 4, 65. [CrossRef] 154. Sharma, V.; Srinivasan, K.; Chao, H.C.; Hua, K.L.; Cheng, W.H. Intelligent deployment of UAVs in 5G heterogeneous communica- tion environment for improved coverage. J. Netw. Comput. Appl. 2017, 85, 94–105. [CrossRef] 155. Khopkar, P. Mixed-Initiative Flexible Autonomy in Drone Swarms for COVID-19 Applications. In Proceedings of the 2020 IEEE International Symposium on Technology and Society (ISTAS), Tempe, AZ, USA, 12–15 November 2020; pp. 457–461. 156. Vattapparamban, E.; Güvenç, I.; Yurekli, A.I.; Akkaya, K.; Uluağaç, S. Drones for smart cities: Issues in cybersecurity, privacy, and public safety. In Proceedings of the 2016 international wireless communications and mobile computing conference (IWCMC), Paphos, Cyprus, 5–9 September 2016; pp. 216–221. 157. Deng, J.; Hua, J.; Adu Gyamfi, B.; Shaw, R. Drones Activity in Epidemic Prevention and Prospects in the Post-COVID-19. In Considerations for a Post-COVID-19 Technology and Innovation Ecosystem in China; Springer: Singapore, 2022; pp. 33–43. 158. Zhang, M.; Chen, Y.; Susilo, W. PPO-CPQ: A Privacy-Preserving Optimization of Clinical Pathway Query for E-Healthcare Systems. IEEE Internet Things J. 2020, 7, 10660–10672. [CrossRef] 159. Martins, B.O.; Lavallée, C.; Silkoset, A. Drone Use for COVID-19 Related Problems: Techno-solutionism and its Societal Implications. Glob. Policy 2021, 12, 603–612. [CrossRef] [PubMed] Micromachines 2022, 13, 1593 34 of 34 160. Garcia, F.; Borghini, F.; Lombardi, M.; Gallone, A.; Ramalingam, S. Emotional analysis of safeness and risk perception of drones during the COVID-19 pandemic in Italy. In Proceedings of the 2021 International Carnahan Conference on Security Technology (ICCST), Hatfield, UK, 11–15 October 2021. 161. He, H.; Gray, J.; Cangelosi, A.; Meng, Q.; McGinnity, T.M.; Mehnen, J. The challenges and opportunities of artificial intelligence in implementing trustworthy robotics and autonomous systems. In Proceedings of the 3rd International Conference on Intelligent Robotic and Control Engineering, Oxford, UK, 10–12 August 2020.