A novel nest-based scheduling method for mobile wireless body area networks

Wireless Body Area Networks (WBANs) comprise various sensors to monitor and collect various vital signals, such as blood pressure, pulse, heartbeat, body temperature, and blood sugar. A dense and mobile WBAN often suffers from interference, which causes serious problems, such as wasting energy and degrading throughput. In reality, not all of the sensors in WBAN need to be active at the same time. Therefore, they can be divided into different groups so that each group works in turn to avoid interference. In this paper, a Nest-Based WBAN Scheduling (NBWS) algorithm is proposed to cluster sensors of the same types in a single or multiple WBANs into different groups to avoid interference. Particularly, we borrow the graph coloring theory to schedule all groups to work using a Time Division for Multimodal Sensor (TDMS) group scheduling model. Both theoretical analysis and experimental results demonstrate that the proposed NBWS algorithm performs better in terms of frequency of collisions, transmission delay, system throughput, and energy consumption compared to the counterpart methods.