Combinatorial Optimization-Based Clustering Algorithm for Wireless Sensor Networks

Yuxiao Cao,Zhen Wang

doi:10.1155/2020/6139704

Abstract

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.

Highlights

As a key part of the Internet of ings (IOT) [1, 2], wireless sensor network (WSN) has been widely utilized in many fields such as industry, agriculture, military, environmental monitoring, transportation, smart home, and so on [3,4,5,6,7,8]
To reduce the node energy consumption and prolong the network lifetime, we propose a combinatorial optimization-based clustering algorithm (COCA) which runs on the base station (BS) for WSN from the point view of combinatorial optimization in this paper
In order to verify the performance of COCA, we simulated COCA using Matlab 2016a and compared the simulation results with three other clustering algorithms, i.e., lowenergy adaptive clustering hierarchy (LEACH), energy centric cluster-based routing protocol for WSN (ECCR), and particle swarm optimization (PSO)-ECHS. e simulation results showed that COCA has better performance than other algorithms in terms of network lifetime, average residual energy of nodes, and network throughput. e reason for choosing LEACH as the comparison algorithm is that LEACH, as the first hierarchical clustering algorithm, has been widely used as a benchmark to evaluate the performance of other algorithms

Summary

Introduction

As a key part of the Internet of ings (IOT) [1, 2], wireless sensor network (WSN) has been widely utilized in many fields such as industry, agriculture, military, environmental monitoring, transportation, smart home, and so on [3,4,5,6,7,8]. The cooperative algorithms reduce the energy consumption of control information exchange, they still cannot get the best clustering result from the perspective of the whole network because there are multiple parameters affecting the clustering and these parameters are mutually conflicting. To reduce the node energy consumption and prolong the network lifetime, we propose a combinatorial optimization-based clustering algorithm (COCA) which runs on the BS for WSN from the point view of combinatorial optimization in this paper. (1) Based on the objective and procedure, the clustering of WSN is abstracted into a combinatorial optimization problem (2) A novel binary particle coding scheme is designed to present the selection of CHs (3) After the CHs are elected, the constraints between parameters which affect clustering are considered comprehensively.

Related Work

Preliminaries and System Model

Combinatorial Optimization-Based Clustering Algorithm

Algorithm Performance Evaluation

Conclusion