Karafan Journal

Karafan Journal

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

Document Type : Original Article

Authors
1 Electrical Engineering Faculty, Technical and Vocational University (TVU), Tehran, Iran.
2 Student, Electrical Engineering Faculty, Technical and Vocational University (TVU), Tehran, Iran.
Abstract
The The Internet of Things network consists of many devices with various characteristics and constraints. One of the major properties of IoT is that it has heterogeneous nodes and communication protocols. Hence, it is a required routing method that decreases the energy consumption of the network. This article proposed a cluster head selection method using improved grey wolf optimization. The goal of the proposed algorithm was to improve load balancing and decrease power consumption. This process method of cluster head selection uses many metrics to improve load balancing and energy consumption. This method has more convergence than traditional grey wolf optimization. The proposed method was implemented by MATLAB 2019a and many simulations were run 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 results of the simulations show that the proposed method improved end-to-end delay, convergence, and energy consumption parameters. It can be concluded that load balancing on IoT networks improved.
Keywords
Subjects

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Volume 21, Issue 1 - Serial Number 66
Engineering & Technical
Spring 2024
Pages 109-131

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