Distributed Energy-Efficient Clustering algorithm for Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) consist of a large number of limited capability (power and processing) Micro Electro Mechanical Systems (MEMS) capable of measuring and reporting physical variables related to their environment. In surveillance applications, sensors are deployed in a certain field to detect and report events like presence, movement, or intrusion in the monitored area. Minimizing energy dissipation and maximizing network lifetime are important issues in the design of applications and protocols for sensor networks. Energy-efficient sensor state planning consists in finding an optimal assignment of states to sensors in order to maximize network lifetime. The existing scheme developed a centralized mechanism for near-optimal state assignment to sensors in large-scale cluster-based monitoring wireless sensor networks. The existing one was based on a tabu algorithm that computes a near-optimal network configuration in which each sensor can be activated, put in sleep mode or promoted as cluster head. The existing mechanism maximizes network lifetime while ensuring the full coverage of the monitored area and the connectivity of the obtained configuration. Connectivity is fulfilled through an optimally computed spanning tree connecting all the cluster heads. Due to abnormal node distribution in case of land surveillance, the existing tabu based optimal energy setting become complex. In addition the tabu algorithm keeps the probabilistic event detection independent for the respective node. To overcome the abnormal node Distribution event detection triviality, distributed energy efficient algorithm is proposed in this work. The proposed work of this thesis, develop a more sophisticated heuristic to improve the network lifetime. The proposed scheme handles distance-dependent probabilistic event detection. The distance based probability is a function of the distance of the corresponding sensor from the event. The proposed system develop distributed algorithm which addresses the energy-efficient clustering under the joint coverage and routing constraint. The experimental simulations are carried for the proposed model using Network Simulator 2 (NS-2) for multiple simulation times, routing topology and energy coverage area.