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Submitted
August 31, 2024
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May 14, 2025
Published
June 1, 2025
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Abstract

The healthcare industry is the most widely used application of Wireless Body Area Network (WBAN). WBAN networks have been developed to provide a more flexible experience than traditional wired medical systems using low-power miniature sensors that monitor physiological signals. Some studies have addressed the problem of congestion in body networks and proposed new methodologies to deal with this problem. Recently, in WBAN networks, metaheuristics algorithms have been employed to improve performance and job execution efficiency, which has proven effective in finding optimal solutions, especially for congestion problems. This research applies a metaheuristic algorithm, the  Firefly algorithm, to optimize node selection in WBAN. Additionally, the Random Early Detection system (RED) is employed to control network congestion. Two scenarios are applied, the first one represents a network with 20 nodes, and the second one represents a smaller network with 10 nodes. The results were compared statistically. The present paper defines an enhanced congestion handling method for WBANs. For this purpose, the fitness function of the nodes is evaluated based on essential factors: congestion probability, the variables are residual energy, average data rate, node distance, and sink distance. It also improves the routing strategy by introducing the firefly algorithm-based forward-looking node selection approach. This eventually results in improved quality of service.

Keywords

Metaheuristic algorithms, Firefly algorithm, RED, WBAN, Qos

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Copyright (c) 2025 The Author(s), under exclusive license to the University of Thi-Qar

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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