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Tunicate swarm Grey Wolf optimization for multi-path routing protocol in IoT assisted WSN networks - 2020

Tunicate swarm Grey Wolf optimization for multi-path routing protocol in IoT assisted WSN networks

Research Area:  Wireless Sensor Networks


Internet of Things (IoTs) have become popular for connected people as well as objects for collecting and exchanging data based on embedded sensors. In IoT-assisted Wireless sensor Networks (WSN), the nodes are considered as the resource parameters in several ways, like computing resources, energy resources, and storage resources such that the robust multipath routing protocols are needed for maintaining long network lifetime and for achieving higher energy utilization. Hence, this paper presents the multipath routing protocol using the proposed optimization method, named Tunicate swarm Grey Wolf optimization (TSGWO) algorithm in the IoT assisted WSN network. By multipath routing protocol, the multipath is designed by multipath source node to several destinations. The multipath source node forwarding packet to multiple destinations simultaneously. At first, the nodes in IoT-assisted WSN network is simulated together and performs the cluster head selection using Fractional Gravitational Search algorithm (FGSA), and then the multipath routing process is done on the basis of proposed TSGWO in which the routing path is selected by considering the fitness parameters, like QoS parameters and trust factors. The QoS parameters include the delay, energy, link lifetime, as well as distance. The path with the minimum distance is selected as optimal path using fitness parameter. The proposed optimization algorithm effectively performs the multipath routing mechanism by integrating the parametric features from both the optimization algorithm. After that, the route maintenance process is carried out in the simulated IoT network to recover the link breakage based on DRINA. The proposed TSGWO outperformed other methods with maximal average Residual energy of 2.161 J, maximal link lifetime of 0.075 s, maximal PDR of 96.38%, and maximal throughput of429.49 Kbps.


Author(s) Name:  Nitesh Chouhan & S. C. Jain

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-020-02657-w

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