Providing a Routing Method in Internet of Things for Better Load Distribution Using the Improved Gray Wolf Optimization Algorithm

Articles in Press

Document Type : Original Article

Authors

1 Department of Electrical and Computer Engineering, Faculty of islamabad ghaeb, kermansha Branch, Technical and Vocational University (TVU),, Iran

2 Student, Electrical Engineering Faculty, Technical and Vocational University, Tehran, Iran

10.48301/kssa.2024.421630.2740

Abstract

Internet of Things network consists of many devices with vary characteristics and constraints. One of the major properties of IoT that it has heterogenous nodes and communication protocols. Hence, it is required routing method that decrease the energy consumption of network. In this article proposed cluster head selection method with using of improved grey wolf optimization. The goal of proposed algorithm is improving load balancing and decreasing power consumption. In this method process of cluster head selection use many metrics to improve load balancing and energy consumption. This method has more convergence than traditional grey wolf optimization. Proposed method implemented by MATLAB 2019a and run many simulations to compare with three other metaheuristic algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), improved artificial bee colony (IABC), and glowworm swarm optimization (GSO). The result of simulations shows that the proposed method improved end to end delay, convergence, energy consumption parameters. It can be concluded that load balancing on IoT network improved.

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Karafan Quarterly Scientific Journal

Articles in Press, Accepted Manuscript
Available Online from 06 April 2024

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History

  • Receive Date: 10 November 2023
  • Revise Date: 07 February 2024
  • Accept Date: 05 April 2024

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