Abstract
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.
Highlights
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 spider monkey optimization (SMO)
Wireless sensor networks (WSNs) accumulate, analyze, and utilize data that are received wirelessly from sensor nodes, which have been used for various applications such as smart homes [1], air purifiers [2], and fire and disaster monitoring [3,4] due to their improved performance, ease of use, and low price
The experiment was conducted both in a homogeneous setup, where the initial energy of all nodes was the same, and in a heterogeneous setup, where the initial energy differed among nodes
Summary
Wireless sensor networks (WSNs) accumulate, analyze, and utilize data that are received wirelessly from sensor nodes, which have been used for various applications such as smart homes [1], air purifiers [2], and fire and disaster monitoring [3,4] due to their improved performance, ease of use, and low price. Improving the battery performance of a node increases costs. The low-energy adaptive clustering hierarchy (LEACH) protocol improves energy efficiency via a clustering method [6]. When data are transmitted from a node to the base station (BS), energy consumption is affected by the distance between them. Clustering reduces the transmission distance of the nodes that are not cluster-heads (CHs), which are those that gather data from neighboring nodes for forwarding. In LEACH, Sensors 2019, 19, 5281; doi:10.3390/s19235281 www.mdpi.com/journal/sensors
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