Misbehavior Node Detection using Hamming Residue Mechanism in Clustering WSN

Nowadays, Sensor nodes are utilized in widespread distribution. Thus, security issues are a significant apprehension in the Wireless Sensor Networks (WSN). Misbehavior node is a highly harmful attack besides the WSNs, and the entire networks are broken, disturbing the data transmission. To solve these problems, Misbehavior node detection using Hamming residue mechanism (MNHM) in Clustering WSN is proposed. This approach uses the Hamming code Word (HCW) to detect the misbehavior nodes in the WSN. Here, the security code likes HCW for detecting misbehavior nodes in the network. This approach is to discover the highest energy efficiency node and lowest distance nodes in the WSN. HCW minimizes network delay and provides end-to-end security. To improve safety, security bits are added. The length of HCW is n, and k denotes the security bits. This approach aims to strengthen the misbehavior node detection in the WSN. Furthermore, it applies an Ant Colony Optimization (ACO) algorithm for solving shortest path issues in the network. The simulation results explain that the proposed method has better residual energy and improved the network lifetime. It also minimizes the network delay and miss detection ratio in the WSN.