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Cellular automata-based optimised routing for secure data transmission in wireless sensor networks
Journal of Experimental & Theoretical Artificial Intelligence ( IF 1.7 ) Pub Date : 2021-02-10 , DOI: 10.1080/0952813x.2021.1882002
Pradeep Sadashiv Khot 1 , Udaykumar Laxman Naik 2
Affiliation  

ABSTRACT

Sensor nodes encompass the capability of controlling and monitoring the environment by measuring the factors, like temperature, pressure, and humidity. Ensuring secure routing in the network is an important and significant task in the wireless sensor network (WSN). Even though various routing protocols are adopted in the network, providing security and maintaining the energy is still a challenging issue in the research community. Thus, an effective particle-based spider monkey optimisation (P-SMO) algorithm is proposed in this research to select the optimal route to manage secure communication in WSNs. Accordingly, the network is simulated and the cluster heads (CHs) are chosen based on the efficient learning automata-based cell clustering algorithm (ELACCA) such that the routing path is established based on the chosen CHs. Thus, the proposed P-SMO algorithm establishes the secure routing path based on the factors, such as energy, delay, consistency factor, and trust. The proposed P-SMO algorithm is the integration of particle swarm optimisation (PSO) and spider monkey optimisation (SMO) algorithm in such a way that the optimal routes are defined effectively. The analysis of the proposed routing protocol proved the effective performance with the maximal number of nodes alive, coverage, energy balancing index, and the average remaining energy of 67, 93.407%, 0.7555, and 0.3402 J, respectively.



中文翻译:

基于元胞自动机的优化路由在无线传感器网络中的安全数据传输

摘要

传感器节点包括通过测量温度、压力和湿度等因素来控制和监测环境的能力。确保网络中的安全路由是无线传感器网络(WSN)中的一项重要而重要的任务。尽管网络中采用了各种路由协议,但提供安全性和维护能量仍然是研究界的一个具有挑战性的问题。因此,本研究提出了一种有效的基于粒子的蜘蛛猴优化(P-SMO)算法来选择管理WSN中安全通信的最佳路径。因此,网络被模拟并且簇头(CHs)基于高效学习自动机的细胞聚类算法(ELACCA)被选择,使得路由路径基于选择的CHs建立。因此,所提出的P-SMO算法基于能量、延迟、一致性因子和信任等因素建立安全路由路径。所提出的 P-SMO 算法是粒子群优化 (PSO) 和蜘蛛猴优化 (SMO) 算法的集成,从而有效地定义了最优路径。对所提出的路由协议的分析证明了有效的性能,最大存活节点数、覆盖范围、能量平衡指数和平均剩余能量分别为 67、93.407%、0.7555 和 0.3402 J。所提出的 P-SMO 算法是粒子群优化 (PSO) 和蜘蛛猴优化 (SMO) 算法的集成,从而有效地定义了最优路径。对所提出的路由协议的分析证明了有效的性能,最大存活节点数、覆盖范围、能量平衡指数和平均剩余能量分别为 67、93.407%、0.7555 和 0.3402 J。所提出的 P-SMO 算法是粒子群优化 (PSO) 和蜘蛛猴优化 (SMO) 算法的集成,从而有效地定义了最优路径。对所提出的路由协议的分析证明了有效的性能,最大存活节点数、覆盖范围、能量平衡指数和平均剩余能量分别为 67、93.407%、0.7555 和 0.3402 J。

更新日期:2021-02-10
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