Minimizing Energy Consumption in Body Sensor Networks via Convex Optimization

Body Sensor Networks (BSNs) consist of miniature sensors deployed on or implanted into the human body for health monitoring. Conserving the energy of these sensors, while guaranteeing a required level of performance, is a key challenge in BSNs. In terms of communication protocols, this translates to minimizing energy consumption while limiting the latency in data transfer. In this paper, we focus on polling-based communication protocols for BSNs, and address the problem of optimizing the polling schedule to achieve minimal energy consumption and latency. We show that this problem can be posed as a geometric program, which belongs to the class of convex optimization problems, solvable in polynomial time. We also introduce a dynamic priority vector for each sensor, based on the observation that relative priorities of sensors in a BSN change over time. This vector is used to develop a decision-tree based approach for resolving scheduling conflicts among devices. The proposed framework is applicable to a broad class of periodic polling-based communication protocols. We design one such protocol in detail and show that it achieves an improvement of approximately 45% over the widely accepted standard IEEE 802.15.4 MAC protocol.