Comparative Analysis of Energy Efficient Techniques in Wireless Sensor Networks
Abstract
Wireless Sensor Networks (WSNs) are made up of many sensor nodes. It is fast growing technology used for effective computation. Wireless sensors are used for border security, disaster management, surveillance, medical field and environment monitoring. Energy efficiency is most important factor in Wireless Sensor Networks (WSNs), since they incorporate limited sized batteries that would not be recharged or replaced. Wireless sensor networks have low power sensor nodes that quickly lose energy. The energy possessed by the sensor nodes must be optimally utilized so as to increase the lifetime. Swarm Intelligence (SI) techniques play an important role in improving WSNs lifetime. In this paper, Glowworm Swarm Optimization (GSO), A new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), Particle swarm Optimization (PSO), Group Search Ant Lion with Levy Flight (GAL-LF), Cuckoo Search (CS), Fruitfly Optimization algorithm (FFOA) and grasshopper Optimization algorithm (GOA) are analyzed and compared in the terms of alive nodes, normalized energy, cluster head distance and delay. Based on this comparative analysis, it becomes easy to propose technique for cluster head selection. Some suggestions have also been given in this research which helps in improving network lifetime.