Design and Modeling of Resource-constrained IoT based Body Area Networks

Abstract

Due to the recent advancement and development of sensing, wireless, and communication technologies, therehas been a shift in attention towards Body Area Networks (BANs). One of the most important services of BAN is the remote monitoring of patients, enabling doctors to observe, diagnose, and prescribe the patients without being physically present. Various vital signs are being monitored by body sensing devices installed inside, on or off the body of patients, but most of these devices are constrained in terms of resources such as storage, processing, bandwidth, and energy due to their smaller size. This paper aims at highlighting the key findings related to BAN applications, constrained resources, and various resource management techniques. The paper also presents the design and modeling of a resource-constrained BAN system and discusses the various scenarios of BAN in the context of resource constraints. It further proposes an Advanced Edge Clustering (AEC) approach to manage the resources such as energy, storage, and processing of BAN devices while performing real-time data capture of critical health parameters and detection of abnormal patterns. The comparison of the AEC approach is done with the Stable Election Protocol (SEP) through simulations and empirical data analysis. The results show an improvement in energy, processing time and storage requirements for the processing of data on BAN devices in AEC as compared to SEP.