AI-Enhanced Optimization Algorithm for Body Area Networks in Intelligent Wearable Patches for Elderly Women's Safety

AI-Enhanced Optimization Algorithm for Body Area Networks in Intelligent Wearable Patches for Elderly Women's Safety

Kswaminathan Kalyanaraman, Sivaram Ponnusamy
Copyright: © 2024 |Pages: 29
DOI: 10.4018/979-8-3693-3406-5.ch004
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Abstract

IoT-enabled sensor nodes gather real-time data and employ machine learning techniques to enable remote monitoring and rapid response. To overcome these challenges, the proposed solution employs the opportunistic power best routine algorithm (OPA), a heuristic algorithm designed to extend the lifespan of sensor nodes in the wearable patches for women's safety. This algorithm eliminates redundant data loops between network patches, ultimately increasing the efficiency of the system. The effectiveness of this approach is evaluated based on metrics such as network lifespan, latency in data sensing, throughput, and error rates. Maximizing power usage through algorithms like OP2A and employing predictive analytics, the system can enhance network efficiency, reduce response times, and ultimately contribute to a safer environment for women.
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Literature Review

The data collection may also be done by some data gathering points between nodes in smart wearable patches in low energy consumption (Hijazi, et al., 2018). But the energy used by the data gathering point is somewhat maximum when compared to other nodes in the wearable patches. In (Deng DJ, et al., 2019) a predefined time slot is given, within the time slot, the data has been forwarded to the collection point of the data. But if any of the nodes have been sending the data beyond the time slot, continuity of the data will not be in the data. This will make a wrong diagnosis on the data received results the n system to be taken a faulty the decision.

The hopping distance between nodes and the health care system collection point is predefined as a one-hop routing distance. The network structure is checked for every data transmission and updated in a distance routing table. As the update is done for every hopping network power backup and network lifetime decrease affecting the overall network efficiency. If the distance will vary, the entire network structure will be changed according to the defined hopping distance. This may also minimize the data transmission quality to be low.

In (Gujral, S., et al., (2017) a grouping-based structure is defined for the data packet transmission, depending on the data hopping distance and power required for the transmission. The drawback of the system is that the network is grouped on the distance and power consumed basis for every data transmission from smart wearable patches.

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