  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.
