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Open AccessArticle

Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization

School of Electrical and Electronics Engineering, Chung-Ang University; 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
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Sensors 2019, 19(23), 5281; https://doi.org/10.3390/s19235281
Received: 20 October 2019 / Revised: 22 November 2019 / Accepted: 27 November 2019 / Published: 30 November 2019
(This article belongs to the Section Sensor Networks)
Extending the lifetime and stability of wireless sensor networks (WSNs) through efficient energy consumption remains challenging. Though clustering has improved energy efficiency through cluster-head selection, its application is still complicated. In existing cluster-head selection methods, the locations where cluster-heads are desirable are first searched. Next, the nodes closest to these locations are selected as the cluster-heads. This location-based approach causes problems such as increased computation, poor selection accuracy, and the selection of duplicate nodes. To solve these problems, we propose the sampling-based spider monkey optimization (SMO) method. If the sampling population consists of nodes to select cluster-heads, the cluster-heads are selected among the nodes. Thus, the problems caused by different locations of nodes and cluster-heads are resolved. Consequently, we improve lifetime and stability of WSNs through sampling-based spider monkey optimization and energy-efficient cluster head selection (SSMOECHS). This study describes how the sampling method is used in basic SMO and how to select cluster-heads using sampling-based SMO. The experimental results are compared to similar protocols, namely low-energy adaptive clustering hierarchy centralized (LEACH-C), particle swarm optimization clustering protocol (PSO-C), and SMO based threshold-sensitive energy-efficient delay-aware routing protocol (SMOTECP), and the results are shown in both homogeneous and heterogeneous setups. In these setups, SSMOECHS improves network lifetime and stability periods by averages of 13.4%, 7.1%, 34.6%, and 1.8%, respectively. View Full-Text
Keywords: WSNs; sampling SMO; energy efficient CH selection; SSMOECHS; network lifetime; network stability WSNs; sampling SMO; energy efficient CH selection; SSMOECHS; network lifetime; network stability
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Lee, J.-G.; Chim, S.; Park, H.-H. Energy-Efficient Cluster-Head Selection for Wireless Sensor Networks Using Sampling-Based Spider Monkey Optimization. Sensors 2019, 19, 5281.

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