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, 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 the intra-BAN (Phase I) and inter-BAN (Phase II) communications, the aggregation approach is cost effective. Extensive simulation results show that quality-driven energy-efficient big data aggregation for WBANs improves network efficiency in terms of traffic served and energy consumption by 5–7 and 7–8% as compared to the existing schemes.
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