Optimal Clone Attack Detection Model using an Energy-Efficient GSA based Simulated Annealing in Wireless Sensor Networks

Abstract

Security of Wireless Sensor Networks (WSN) is an essential challenge as the sensors are deployed in open environment and they are prone to various attacks. One among them is the clone attack. Hence clone node discovery is a vital challenge in WSN. Various clone node discovery approaches have been projected to sense these clone nodes. These approaches deserve control overheads and the finding accuracy is low when the clone is chosen as a witness node. In this proposed method, a Residual Energy and Gravitational Search Algorithm based Simulated Annealing (RE-GSASA) for identifying and isolating the clone node in WSN is investigated. The performance of the proposed model is calculated using detection rate, packet delivery rate and sensor node density. The results show that the effectiveness of RE-GSASA method in terms of optimized energy consumption and improves clone node detection probability rate by minimizing the clone node detection time. The implementation is done using ns-2 to exhibit the actuality of the proposed work.