Genetic Algorithm Based Clustering Techniques in Wireless Sensor Networks: A Comprehensive Study

Abstract

Wireless sensor network consist of small sensing devices also known as nodes capable of sensing environmental and physical parameters like temperature, humidity etc. and send the information to a central node known as sink or base station. Since the sensors are limited in energy, minimizing the energy dissipation and increasing the network lifetime is the key challenge faced by wireless sensor networks. In WSN, cluster based routing schemes are used for reducing energy consumption by data aggregation at intermediate nodes. Various traditional and meta-heuristic approaches for clustering are already implemented. But finding an optimal clustering and routing path is an NP-hard problem. Meta-heuristic algorithms such as genetic algorithm can be used in large scale wireless sensor networks to find the optimal clustering and routing scheme. In this paper a detailed study of state-of-the art genetic algorithm based clustering techniques in wireless sensor networks with their objective, characteristics etc. are presented. Different parameters used for the construction of the fitness function are also analyzed.