Journal
MEDICAL ENGINEERING & PHYSICS
Volume 110, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.medengphy.2022.103922
Keywords
Medical application; Wireless body area networks; Biomedical sensors; Bioinspired particle swarm optimization; Iterative heuristic chicken swarm optimization
Categories
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) by the Min-istry of Education
- [NRF-2022R1F1A1066602]
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Healthcare, sports, the military, location monitoring, and wireless body networks are emerging as important technologies. Reliable and effective transmission of data packets containing vital signs is crucial in the widespread use of biomedical sensor networks in medical applications. The main challenges in wireless body area networks (WBAN) are efficient data transport and limited energy supply. This research improves the routing algorithm using genetic heuristics and optimization approaches, thus extending the network lifetime.
Healthcare, sports, the military, location monitoring and wireless body networks are emerging as technology of major relevance. As a result of the widespread usage of biomedical sensor networks in medical applications, it is essential that data packets containing vital signs be reliably and effectively supplied to the medical center. Because of its mobility, real-time monitoring, cheap cost, and real-time feedback, it may be used in a broad variety of applications. Effective data transport and a limited energy supply are the main challenges in WBAN. Uses genetic heuristics to enhance routing algorithm efficiency. Low-energy adaptive clustering hierarchy (LEACH) and distributed energy efficiency clustering (DEC) are two kinds of clustering algorithms (DEEC). A clustering-based routing protocol may be optimized using this study's optimization approach so that the network's lifetime can be extended.. The cluster heads (CHs) in sensor nodes are picked with the least amount of overhead grading possible. The cluster is being balanced. Passive clustering based on Bioinspired Particle Swarm Optimization (BPSO) should be used for clustering purposes. Routing messages efficiently means sending them quickly and efficiently without using a lot of bandwidth. Using constraints such as distance and residual power, the optimal path for the cluster may be determined with the help of iterative and heuristic chicken swarm optimization (IHCSO) for short. An evaluation of the packet distribution allocation, capacity, and average end-to end latency illustrates the practicability of the proposed system in research concerning its efficiency. According to the findings of the research, following the technique that was proposed leads to much better outcomes.
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