A Hyper Heuristic Localization Based Cloned Node Detection Technique Using GSA Based Simulated Annealing in Sensor Networks

Due to inadequate energy resources, data aggregation from multiple sensors in Wireless Sensor Networks (WSN) is typically accomplished by clustering. But such data aggregation is recognized to be highly susceptible to clone attacks owing to the unattended nature of the network. Thus, ascertaining trustiness of the sensor nodes is crucial for WSN. Though numerous methods for cloned attack node isolation are provided in recent years, energy efficiency is the most significant issues to be handled. In this work, a Residual Energy and GSA based Simulated Annealing (RE-GSASA) for detecting and isolating the cloned attack node in WSN is given. Residual Energy-based Data Aggregation in WSN initially uses residual energy because the basis to perform aggregation technique with the sensor node possessing the maximum residual energy as the Cluster Head (CH). Next, Location-based Cloned attack on cluster nodes is given to enhance the clone detection probability rate. Here, the location and residual energy is taken into account to identify the presence of cloned attack nodes within the network. Finally, Gravitational Search Algorithm with global search ability is investigated to identify the cloned attack nodes and performs isolation through local optimal simulated annealing model. Simulation results demonstrate that RE-GSASA provides optimized energy consumption and improves cloned attack detection probability by minimizing the cloned attack detection time.