A trusted distributed routing scheme for wireless sensor networks using blockchain and meta-heuristics-based deep learning technique,Transactions on Emerging Telecommunications Technologies

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A trusted distributed routing scheme for wireless sensor networks using blockchain and meta-heuristics-based deep learning technique
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2021-04-02 , DOI: 10.1002/ett.4259
M Revanesh ₁ , Venugopalachar Sridhar ₂

Affiliation

The 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.

中文翻译：

基于区块链和基于元启发式的深度学习技术的无线传感器网络可信分布式路由方案

具有波动环境的无线传感器网络 (WSN) 可能容易受到各种类型的恶意网络攻击，并且它们主要依赖于身份验证和加密算法来应对这一挑战。由于路由信息的实时变化，文献中的大多数主要路由方案在表征网络上的恶意节点时都采用回退方式。因此，本文提出了一种基于区块链、元启发式和深度学习算法的可靠且可信赖的互相关路由方案。WSN中传播的路由信息由区块链策略处理，其中最佳路由是在Salp Swarm优化算法的帮助下完成的。设想节点之间的路由信息变化，并通过使用深度卷积神经网络算法来完成最佳路由决策。所提出的路由方案在 NS2 中实现，并根据延迟、能耗和吞吐量指标评估其性能。该方法的效率提高了97%，并对恶意攻击、延迟和延迟进行了评估。将现有方法如粒子群优化、马尔可夫决策过程、基于安全不相交路由的验证消息、基于可信集群的路由和基于强化学习的神经网络 (RLNN) 与提出的延迟比方法进行比较. 所提出的路由方案在 NS2 中实现，并根据延迟、能耗和吞吐量指标评估其性能。该方法的效率提高了97%，并对恶意攻击、延迟和延迟进行了评估。将现有方法如粒子群优化、马尔可夫决策过程、基于安全不相交路由的验证消息、基于可信集群的路由和基于强化学习的神经网络 (RLNN) 与提出的延迟比方法进行比较. 所提出的路由方案在 NS2 中实现，并根据延迟、能耗和吞吐量指标评估其性能。该方法的效率提高了97%，并对恶意攻击、延迟和延迟进行了评估。将现有方法如粒子群优化、马尔可夫决策过程、基于安全不相交路由的验证消息、基于可信集群的路由和基于强化学习的神经网络 (RLNN) 与提出的延迟比方法进行比较.

更新日期：2021-04-02

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