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.
@article{arxiv.2206.05699,
title = {Quality-Driven Energy-Efficient Big Data Aggregation in WBANs},
author = {Amit Samanta and Tri Gia Nguyen},
journal= {arXiv preprint arXiv:2206.05699},
year = {2022}
}