International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

Fractional Gaussian Firefly Algorithm and Darwinian Chicken Swarm Optimization for IoT Multipath Fault-Tolerant Routing

Author NameAuthor Details

Salem Abdulla Awadh Ba hmaid, V. Vasanthi

Salem Abdulla Awadh Ba hmaid[1]

V. Vasanthi[2]

[1]Computer Science Department, Rathinam College of Arts and Science, Bharathiar University, Coimbatore, India

[2]Department of Information Technology, Sri Krishna Adithya College of Arts and Science, Bharathiar University, Coimbatore, India

Cite this Article

DOI: 10.22247/ijcna/2020/205318

Pages : 167 - 177

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Abstract

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.

Index Terms

Internet-of-Things

Wireless Sensor Networks

Fault Tolerance

Energy Constraint Problem

Fractional Gaussian Firefly algorithm

Darwinian Chicken Swarm Optimization.

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