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arXiv · 2206.05699

Quality-Driven Energy-Efficient Big Data Aggregation in WBANs

Amit Samanta·Tri Gia Nguyen
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Abstract

In the Internet-of-Things (IoT) era, the development of Wireless Body Area Networks (WBANs) and their applications in big data infrastructure has gotten a lot of attention from the medical research community. Since sensor nodes are low-powered devices that require heterogeneous Quality-of-Service (QoS), managing large amounts of medical data is critical in WBANs. Therefore, effectively aggregating a large volume of medical data is important. In this context, we propose a quality-driven and energy-efficient big data aggregation approach for cloud-assisted WBANs. For both intra-BAN (Phase I) and inter-BAN (Phase II) communications, the aggregation approach is cost-effective. Extensive simulation results show that the proposed approach DEBA greatly improves network efficiency in terms of aggregation delay and cost as compared to existing schemes.

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Amit Samanta, Tri Gia Nguyen. 2022-06-12. Quality-Driven Energy-Efficient Big Data Aggregation in WBANs. https://arxiv.org/abs/2206.05699

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