Wearable computing going green: Energy-optimal data transmission for multi-sensor wearable devices

Energy management stands out as a crucial design and operational factor for the emerging wearable devices. In this paper, we investigate how to reduce the data-transmission energy for multi-sensor wearable devices over stochastic wireless channels. Specifically, we formulate the data-transmission scheduling issue into a constrained optimization problem, with an objective to minimize the time-average energy cost under the constrains of limited queueing buffer size. We adopt the canonical Lyapunov optimization framework to derive an online algorithm to minimize the drift-plus-penalty function. Under this framework, we first characterize a fundamental trade-off between energy cost and data-transmission delay with both theoretical performance bounds and numerical verifications. We then reveal a threshold effect of the total buffer size on the energy consumption, below which the buffer size limit will be active and the energy cost rises as the buffer size decreases. We further elaborate the relationship between channel gain and energy consumption. Finally, compared to a random transmission algorithm, our approach can save up to 85.08% of energy.