Differential Evolution with Artificial Bee Colony Optimization Algorithm based Sink Hole Detection in Wireless Sensor Networks

Wireless Sensor Networks (WSN) comprises a group of sensors, commonly employed for data gathering and tracking applications. The design of WSN is prone to the sinkhole attack, where the compromised node tried attracting the network traffic by broadcasting the fake routing updates. An easier authentication process is inadequate preventing WSN from sinkhole attacks as signed routing could also be effortlessly carried out by compromised nodes. To prevent the WSN from sinkhole attack, this paper presents an enhanced artificial bee colony based sinkhole detection (EABC-SHD) algorithm. The EABC-SHD algorithm is based on the foraging behavior of bees and the local optimal problem of ABC has been resolved by differential evolution (DE). Besides, the proposed EABC-SHD algorithm will be executed on the cluster heads, where the choice of CHs is done using a fuzzy logic based mechanism. The clustering process is based on five parameters such as residual energy, distance to BS, distance to neighbors, trust factor and node degree. The proposed EABC-SHD model requires only a minimum amount of time to identify the compromised node, which leads to minimum packet loss and maximum throughput. A detailed simulation analysis is carried out and the results ensured the effective performance of the EABC-SHD algorithm over the compared methods under several aspects