Turkish Journal of Electrical Engineering and Computer Sciences




In recent years, the use of wireless sensor networks (WSNs) has increased and there have been significant improvements in this field. Especially with smarter, cheaper, and smaller sensor nodes, various kinds of information can be detected and collected in different environments and under different conditions. WSNs have thus been used in many applications such as military, surveillance, target tracking, home, medical, and environmental applications. As the popularity of WSNs increases, problems related to these networks are being realized. The dynamic deployment problem is one of the main challenges that have a direct effect on the performance of WSNs. In this study, a novel optimization technique named the quick artificial bee colony (qABC) algorithm was applied to the dynamic deployment problem of WSNs. qABC is a new version of the artificial bee colony algorithm (ABC) and it redefines the onlooker bee phase of ABC in a more detailed way. In order to see the performance of qABC on this problem, WSNs that include only mobile sensors or both stationary and mobile sensors were considered with binary and probabilistic detection models. Some experimental studies were conducted for tuning the colony size ($CS$) and neighborhood radius ($r$) parameters of the qABC algorithm, and the performance of the proposed method was compared with the standard ABC algorithm and some other recently introduced approaches including a parallel ABC, a cooperative parallel ABC, a version of ABC powered by a transition control mechanism (tlABC), and a parallel version of tlABC. Additionally, some CPU time analyses were provided for qABC and ABC considering different dimensions of the problem. Simulation results show that the qABC algorithm is an effective method that can be used for the dynamic deployment problem of WSNs, and it generally improves the convergence performance of the standard ABC on this problem when $r \geq 1$.


Quick artificial bee colony algorithm, wireless sensor networks, dynamic deployment problem, probabilistic detection model, binary detection model

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