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.
Highlights
AWIRELESS SENSOR Network (WSN) is one of the most widely used advanced communication technologies and have numerous application areas such as personal health monitoring [1], military surveillance [2], forest wildfire monitoring [3], air pollution monitoring [4], and moving object tracking [5].As one of the main design issues in Wireless Sensor Networks (WSNs), a sensor deployment plan that places individual sensors in a given region should be provided
The results obtained showed the effectiveness of using selfadaptive differential evolution algorithm (SADE) for the purpose of solving the connected target coverage problem for WSNs
This section clearly explains how these algorithm components are designed to solve connected target coverage optimization problems for WSNs. 1) Representation of solutions A solution to the problem should provide the locations of each individual sensor
Summary
AWIRELESS SENSOR Network (WSN) is one of the most widely used advanced communication technologies and have numerous application areas such as personal health monitoring [1], military surveillance [2], forest wildfire monitoring [3], air pollution monitoring [4], and moving object tracking [5]. Connectivity is another critical requirement for WSNs, and without it, it is not possible to transfer information, no matter how high the coverage rate is. Self-adaptive differential evolution (SADE) [20], which is one of the main adaptive metaheuristic algorithms in continuous optimization domain, is used to solve the connected target coverage optimization in WSNs for the first time in the literature. The results obtained showed the effectiveness of using SADE for the purpose of solving the connected target coverage problem for WSNs. The organization of the remaining sections of this paper is as follows.
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