Quality-Driven Energy-Efficient Big Data Aggregation in WBANs

TL;DR

This paper presents a novel, energy-efficient data aggregation method for WBANs that enhances network performance and reduces delay and cost, crucial for medical data management in IoT environments.

Contribution

It introduces a quality-driven aggregation approach tailored for cloud-assisted WBANs, optimizing both intra- and inter-BAN communications.

Findings

Significantly reduces aggregation delay

Lowers network cost

Improves overall network efficiency

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.