Detection of Wormhole Attack Via Bio-Inspired Ant Colony Optimization Based Trust Model in WSN Assisted IoT Network

Wireless sensor networks are always a demanding field, no matter how technology advances in recent times. Energy efficiency and security are the major concerns for such networks. While energy efficiency has been taken care of using clustering techniques by many researchers in the past, those energy-efficient clustering techniques still do not focus on security issues. Many researchers in the past considered trust models for the sensor networks that prioritize the detection of packet-dropping malicious nodes by observing the packet forwarding behavior of the nodes in a promiscuous way. This work focuses on the wormhole attack in clustered WSNs for Internet of Things networks and proposes a bio-inspired Ant Colony Optimization trust model to counter the wormhole attack. The proposed model computes the direct trust of the nodes based on the pheromone concentration rather than packet forwarding behavior to avoid packet loss. The proposed model has been compared with existing techniques based on packet delivery ratio, number of packet drops, throughput, remaining energy of the network, average end-to-end delay, average control overhead, average energy consumption per node, and average jitter. Simulation has been done in network simulator 2.35 and results prove that the proposed model can detect the malicious nodes in an energy-efficient way and also causes fewer packet drops.