A trusted distributed routing scheme for wireless sensor networks using blockchain and meta‐heuristics‐based deep learning technique

Abstract

AbstractThe wireless sensor network (WSN) with fluctuating environs might be susceptible to diverse types of malicious cyber‐attacks, and they are mostly dependent on the authentication and encryption algorithm to astound this challenge. Most predominant routing schemes in literature are fall backs in characterizing the malicious nodes on networks due to the real time variation of routing information. Therefore, a reliable and trustworthy inter‐correlated routing scheme based on Block chain, Meta‐heuristic, and Deep Learning Algorithms are presented in this paper. The disseminated routing info in the WSN is handled by Block chain strategy, in which the optimal routing is accomplished with the help of Salp Swarm Optimization algorithm. The routing info variations between the nodes are envisaged and the optimal routing decisions are done by using the Deep Convolutional Neural network algorithm. The proposed routing scheme is implemented in NS2 and its performance is evaluated based on latency, energy consumption, and throughput metrics are analyzed. The efficiency of the method is improved as 97% and the evaluation is done for the malicious attacks, latency, and the delay. The comparison is made for the existing methods as particle swarm optimization, Markov decision process, security disjoint routing‐based verified message, trusted‐cluster–based routing, and reinforcement learning‐based neural network (RLNN) with the proposed method for the delay ratio.