Lifetime Maximization in Wireless Sensor Networks using Tabu Swarm Optimization

Abstract

Wireless sensor networks (WSNs) consist of autonomous nodes for monitoring an environment. Several concerns in sensor networks are formulated as multidimensional optimization problems, and approximated through computational intelligence techniques. Routing in Sensor Networks is based on energy optimization and Quality of Service. In this paper a static clustered architecture is developed for energy efficient routing in Sensor Networks using a new version of swarm intelligence namely Tabu Swarm Optimization comprising Particle Swarm Optimization and Tabu Search. The Hybrid approach is efficiently utilized to minimize the energy consumption and prolong the lifetime of distributed sensor network. The results obtained are compared with the previous techniques, to validate ease of computation. Extensive experimental results show that the proposed approach is efficient in clustering, energy minimization, and lifetime.