Optimizing Connected Target Coverage in Wireless Sensor Networks Using Self-Adaptive Differential Evolution

Year 2020, Volume: 8 Issue: 4, 325 - 330, 30.10.2020

Osman Gökalp

https://doi.org/10.17694/bajece.624527

Cited By: 1

Abstract

Wireless Sensor
Networks (WSNs) are advanced communication technologies with many real-world
applications such as monitoring of personal health, military surveillance, and
forest wildfire; and tracking of moving objects. Coverage optimization and
network connectivity are the critical design issues for many WSNs. In this
study, the connected target coverage optimization in WSNs is addressed and it
is solved using self-adaptive differential evolution algorithm (SADE) for the
first time in literature. A simulation environment is set up to measure the
performance of SADE for solving this problem. Based on the experimental
settings employed, the numerical results show that SADE is highly successful
for dealing with connected target coverage problem and can produce higher
performance in comparison with other widely-used metaheuristic algorithms such
as classical DE, ABC, and PSO.

Keywords

Connected Target Coverage, Metaheuristics, Optimization, Self-Adaptive, Wireless Sensor Networks

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