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The increasing penetration of wearable and implantable devices necessitatesenergy-efficient and robust ways of connecting them to each other and to thecloud. However, the wireless channel around the human body poses uniquechallenges such as a high and variable path-loss caused by frequent changes inthe relative node positions as well as the surrounding environment. An adaptivewireless body area network (WBAN) scheme is presented that reconfigures thenetwork by learning from body kinematics and biosignals. It has very lowoverhead since these signals are already captured by the WBAN sensor nodes tosupport their basic functionality. Periodic channel fluctuations in activitieslike walking can be exploited by reusing accelerometer data and schedulingpacket transmissions at optimal times. Network states can be predicted based onchanges in observed biosignals to reconfigure the network parameters in realtime. A realistic body channel emulator that evaluates the path-loss foreveryday human activities was developed to assess the efficacy of the proposedtechniques. Simulation results show up to 41% improvement in packet deliveryratio (PDR) and up to 27% reduction in power consumption by intelligentscheduling at lower transmission power levels. Moreover, experimental resultson a custom test-bed demonstrate an average PDR increase of 20% and 18% whenusing our adaptive EMG- and heart-rate-based transmission power controlmethods, respectively. The channel emulator and simulation code is madepublicly available at https://github.com/a-moin/wban-pathloss.

Adaptive Body Area Networks Using Kinematics and Biosignals