Combinatorial Optimization-Based Clustering Algorithm for Wireless Sensor Networks

Abstract

<jats:p>As node energy of wireless sensor networks (WSN) is limited and cannot be supplemented after exhaustion, clustering algorithm is frequently taken as an effective method to prolong the lifetime of WSN. However, the existing clustering algorithms have some drawbacks, either consuming excessive energy as a result of exchanging too much controlling information between nodes, or lacking a comprehensive perspective in terms of the balance among several conflicting objectives. In order to overcome these shortcomings, a novel combinatorial optimization-based clustering algorithm (COCA) for WSN is proposed in this paper. Different from the above mentioned algorithms which take clustering as a continuous optimization problem, COCA solves the clustering problem from the perspective of combinatorial optimization. Firstly, the clustering of WSN is abstracted into a combinatorial optimization problem. Then, the binary particle coding scheme of cluster head is proposed, which is based on the corresponding relationship between nodes and particle position vectors, and the fitness function is designed according to the parameters used in the process of cluster formation. Finally, the binary particle swarm optimization algorithm is applied to implement the clustering. COCA is validated under different scenarios compared with three other clustering algorithms. The simulation results show that COCA has better performance than its comparable algorithms.</jats:p>

Grey wolf optimization with fuzzy logic for energy‐efficient communication in wireless sensor network‐based Internet of Things scenario

“…In Cao and Wang, 19 An advanced approach named as combinatorial optimization‐based clustering algorithm (COCA) for WSN was suggested in this paper to solve optimization problems. Clustering was considered a combinatorial problem of optimization, and the binary particle coding scheme of CH was suggested.…”

Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

“…It is worth mentioning that the cluster head node of the traditional network is arbitrarily selected, without considering factors such as transmission distance, which will cause excessive power consumption of public nodes [4][5][6]. In addition, some large-scale and vulgar mobile smart sensor networks have complex structures and changeable topologies.…”

A Chaotic Elite Niche Evolutionary Algorithm for Low‐Power Clustering in Environment Monitoring Wireless Sensor Networks

“…Within the traditional clustering algorithm, the choice associated with the cluster head will be arbitrary during the establishment phase regarding the cluster [6][7][8]. The influence factors such as the point transmission distance are not considered, resulting in an improper selection of the cluster head as well as excessive power usage of the common nodes in EMWSNs.…”