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    A Comprehensive Analysis of Energy Dissipation in LEACH Protocol for Wireless Sensor Networks
    (IEEE, 2021) Fadhel, Hajer Faris; Mahmood, Musaria Karim; Al-Omari, Osama
    Wireless Sensor Networks (WSNs) are deployed over a specific area to monitor a range of environmental and physical phenomena. WSNs are known by their limitation in term of energy, memory, and computing capability. Therefore, these sensors need effective wireless communication protocols in terms of energy efficiency, ease of deployment, and low latency. LEACH is the principle routing algorithm adopted today for WSN data transmission. It is the origin of a variety of self-organizing clustering-based routing protocol. Data communication is the main source of energy losses beside but to a lesser extent data processing. The actual research is a step by step follow of LEACH focusing into the energy dissipation in various stages. The simulation of the protocol is accomplished through a MATLAB implementation to clarify the dissipation process. Nodes die after the residual energy has reached a minimum allowed threshold necessary for sensor functionality as part of the WSN. LEACH shows good performances but requires improvement in the cluster head selection stage by the use optimization techniques.
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    Novel Hybrid Invasive Weed Optimization and Machine Learning Approach for Fault Detection
    (IEEE, 2021) Ibrahim, Alasmer; Anayi, Fatih; Packianather, Michael; Al-Omari, Osama
    Fault diagnosis of anomalies in induction motors is essential to ensure industry safety. This paper presents a new hybrid Invasive Weed Optimization and Machine Learning approach for fault diagnosis in an induction motor. The vibration signal provides a lot of information about the motor's operating conditions. Therefore, the vibration signal of the motor was chosen to investigate the fault diagnosis. Two identical 400-V, 50-Hz, 4-pole 0.75 HP induction motors were under healthy, mechanical, and electrical faults tested in a laboratory with different loading. A hybrid model was developed using the vibration signal, the Invasive Weed Optimization algorithm (IWO), and machine learning classifiers. Some statistical features were extracted from the signal using Discrete Wavelet Transform (DWT). The invasive weed optimization algorithm (IWO) was utilized to reduce the number of the extracted features and select the most suitable ones. Then, three classification algorithms namely k-Nearest Neighbor neural network (KNN), Support Vector Machine (SVM), and Random Forest (RF), were trained using k-fold cross-validation and tested to predict the true class. The advantage of combining these techniques is to reduce the training time and increase the average accuracy of the model. The performance of the proposed fault diagnosis model was evaluated by measuring the Specificity, Accuracy, Precision, Recall, and F1_score. The experimental results prove that the proposed model has achieved more than 99.90% of accuracy. Furthermore, the other evaluation parameters also show the same representation of performance. The hybrid model has proved successfully its robust for diagnosing the faults under different load conditions.