Abstract
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.
Highlights
W EARABLE and implantable devices are becoming more prevalent in people’s everyday lives with applicationsManuscript received December 10, 2019; revised March 29, 2020 and May 7, 2020; accepted June 16, 2020
We propose an adaptive network methodology that learns from body kinematics and biosignals, predicts the wireless channel behavior based on them, and subsequently reconfigures the network to achieve both energy efficiency and robustness
We present an adaptive transmission power control (TPC) method based on biosignals such as EMG and heart rate (HR) that are recorded in specific wireless body area network (WBAN) applications
Summary
W EARABLE and implantable devices are becoming more prevalent in people’s everyday lives with applications. Manuscript received December 10, 2019; revised March 29, 2020 and May 7, 2020; accepted June 16, 2020. Date of publication June 22, 2020; date of current version March 5, 2021. Support was received from sponsors of Berkeley Wireless Research Center. These devices must communicate with each other and with the cloud as an integral part of the Internet of Things (IoT), highlighting the need for a network infrastructure around the human body, traditionally named wireless body area network (WBAN) [1] and more recently the Human Intranet [2]
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