1 research outputs found
Adaptive Body Area Networks Using Kinematics and Biosignals
The increasing penetration of wearable and implantable devices necessitates
energy-efficient and robust ways of connecting them to each other and to the
cloud. However, the wireless channel around the human body poses unique
challenges such as a high and variable path-loss caused by frequent changes in
the relative node positions as well as the surrounding environment. An adaptive
wireless body area network (WBAN) scheme is presented that reconfigures the
network by learning from body kinematics and biosignals. It has very low
overhead since these signals are already captured by the WBAN sensor nodes to
support their basic functionality. Periodic channel fluctuations in activities
like walking can be exploited by reusing accelerometer data and scheduling
packet transmissions at optimal times. Network states can be predicted based on
changes in observed biosignals to reconfigure the network parameters in real
time. A realistic body channel emulator that evaluates the path-loss for
everyday human activities was developed to assess the efficacy of the proposed
techniques. Simulation results show up to 41% improvement in packet delivery
ratio (PDR) and up to 27% reduction in power consumption by intelligent
scheduling at lower transmission power levels. Moreover, experimental results
on a custom test-bed demonstrate an average PDR increase of 20% and 18% when
using our adaptive EMG- and heart-rate-based transmission power control
methods, respectively. The channel emulator and simulation code is made
publicly available at https://github.com/a-moin/wban-pathloss.Comment: Accepted for publication in IEEE Journal of Biomedical and Health
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