Performance analysis of wireless sensor networks using artificial bee colony algorithm

The various sensor nodes in a wireless sensor network (WSN) have restricted energy resources which severely affect the long time performance of the network. So, the current research focus is to design energy efficient algorithms for WSNs for improving network lifetime. This paper proposes artificial bee colony (ABC) algorithm with a clustering model to improve the energy capability of the network. The ABC algorithm can improve the internal dynamics of the cluster head nodes and sensor nodes in the WSN. The proposed algorithm can reduce the energy dissipation of nodes, balance the energy consumption across nodes and maximize the lifetime of the network. The ABC algorithm has less number of control parameters in the objective function compared to other algorithms, so it is simple to implement in clustered sensor network. The simulation results prove the superiority of the ABC algorithm compared to other algorithms in maximizing the energy efficiency and longevity of the network.

I. Introduction

Currently, Wireless sensor network (WSN) has emerged as one of the most advanced technologies because of the overwhelming growth in wireless communication technology and 5^th generation wireless technologies. WSNs have various applications in the field of environmental monitoring, security surveillance for large areas, health care automation and local object tracking etc. Usually, WSNs contain multiple sensor nodes in a sensor field for various activities like sensing, observing and communicating various events [1]. These distributed sensor nodes convert the event based information into data packets and transmit them to the network administrator through sink node by cluster based networks. Currently, due to huge data traffic, these sensor nodes are experiencing various constraints such as limited memory, marginal battery life and less transmission power affecting lifetime of the networks. So, the focus of the recent research is to improve the energy efficiency of the WSNs to maximize the network lifetime by employing energy efficient clustering algorithms.