Energy Aware Routing in the Internet of Things using improved Grasshopper Metaheuristic Algorithm

Document Type : Original Article

Authors

1 PhD student, Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

2 Associate Professor, Department of Computer Engineering, Neyshabor Branch, Islamic Azad University, Neyshabor, Iran

3 Assistant Professor, Department of Computer Engineering, Neyshabor Branch, Islamic Azad University, Neyshabor, Iran

Abstract

In most Internet of Things (IoT) applications, network nodes are limited in terms of energy source. Therefore, the need for innovative methods to eliminate energy loss which shortens the life of networks is fully felt in such networks. One of the optimization techniques of energy consumption on the Internet of things is efficient energy routing that the required energy can be reduced by choosing an optimal path. In this paper, an informed or efficient energy approach is proposed for routing on the Internet of Things in which focus is on the sleep-wake schedule of nodes; therefore, a new optimization algorithm called chaos fuzzy grasshopper optimization algorithm was used. In chaos fuzzy grasshopper algorithm, the initial population of grasshoppers is generated by Lorenz chaos theory and the input and output parameters of the algorithm are adjusted by fuzzy approach. To evaluate the efficiency of the proposed method, three criteria of evaluation of remaining energy, network life and coverage rate were used. Investigating the findings in two different scenarios (efficiency over time and efficiency per number of different nodes) showed that the proposed method always is better than the base methods in all scenarios and for all performance evaluation criteria. So that in the study of the death of 30% of nodes which indicates the life of the network, results showed that the proposed method of the paper (FLGOA) has 9% better efficiency than FGOA, 12% better than GOA and 16% better than GSO. Also, the findings about the remaining energy of the network showed that the proposed method has 16% better efficiency than FGOA method, 21% better than GOA and 22% better than GSO. Finally, studies in the coverage rate evaluation criterion showed that the proposed method has 12% coverage rate better than FGOA method, 15% better than GOA and 16% better than GSO.

Keywords

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References

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Electronic and Cyber Defense
Volume 11, Issue 1 - Serial Number 41
No. 41, Spring
May 2023
Pages 15-29

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History

  • Receive Date: 12 December 2021
  • Revise Date: 01 May 2022
  • Accept Date: 24 December 2022
  • Publish Date: 22 May 2023

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