文章基本信息

标题：Fractional Gaussian Firefly Algorithm and Darwinian Chicken Swarm Optimization for IoT Multipath Fault-Tolerant Routing
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作者：Salem Abdulla Awadh Ba hmaid ; V.Vasanthi
期刊名称：International Journal of Computer Networks and Applications (IJCNA)
电子版ISSN：2395-0455
出版年度：2020
卷号：7
期号：6
页码：167-177
语种：English
出版社：EverScience Publications
摘要：Wireless Sensor Networks (WSN) based Internet-of-Things (IoT) systems offer high efficient data transmission with enhanced Quality of Service (QoS). A multi-constraint based energy-efficient and fault-tolerant routing algorithm using Fractional Gaussian Firefly Algorithm (FGFA) and Darwinian Chicken Swarm Optimization (DCSO) are presented for performing optimal multipath communication. FGFA is an improved Firefly Algorithm in which the fractional theory and Gaussian function are incorporated to improve the convergence speed with higher efficiency. Likewise, the DCSO is an improved model of CSO based on the survival theory of Darwin to decrease the computation time and improve the convergence by eliminating the local optimal challenges. Initially, the network is clustered and the cluster heads (CH) are chosen optimally by FGFA based on the objective function with multiple QoS constraints. Then the best routing paths are chosen by DCSO through similar objective function with inter-cluster and intra-cluster delay additionally included. The optimal paths are sorted in a hierarchical order from which multiple paths are utilized for data communication. The FGFA+DCSO routing protocol is assessed in NS-2 simulator and the outcomes shown the proficiency of the suggested approach with 6.3% reduced delay, 6% improved throughput, 26.7% minimized energy, 11% increased lifetime, 20% higher PSNR, and hop count reduced by 1.
关键词：Internet-of-Things;Wireless Sensor Networks;Fault Tolerance;Energy Constraint Problem;Fractional Gaussian Firefly algorithm;Darwinian Chicken Swarm Optimization.