Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques
Özet
Wireless sensor networks (WSNs) are characterized by their ability
to monitor physical or chemical phenomena in a static or dynamic location
by collecting data, and transmit it in a collaborative manner to one or more
processing centers wirelessly using a routing protocol. Energy dissipation is
one of the most challenging issues due to the limited power supply at the sensor
node. All routing protocols are large consumers of energy, as they represent
the main source of energy cost through data exchange operation. Clusterbased hierarchical routing algorithms are known for their good performance
in energy conservation during active data exchange in WSNs. The most
common of this type of protocol is the Low-Energy Adaptive Clustering
Hierarchy (LEACH), which suffers from the problem of the pseudo-random
selection of cluster head resulting in large power dissipation. This critical
issue can be addressed by using an optimization algorithm to improve the
LEACH cluster heads selection process, thus increasing the network lifespan.
This paper proposes the LEACH-CHIO, a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer (CHIO)
algorithm. CHIO is a newly emerging human-based optimization algorithm
that is expected to achieve significant improvement in the LEACH cluster
heads selection process. LEACH-CHIO is implemented and its performance
is verified by simulating different wireless sensor network scenarios, which
consist of a variable number of nodes ranging from 20 to 100. To evaluate
the algorithm performances, three evaluation indicators have been examined,
namely, power consumption, number of live nodes, and number of incoming
packets. The simulation results demonstrated the superiority of the proposed
protocol over basic LEACH protocol for the three indicators.
Cilt
72Sayı
3Bağlantı
https://hdl.handle.net/11363/5715Koleksiyonlar
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