Abstract — We present a novel sampling method to overcome the tradeoff between sensing fidelity and energy-efficiency in the context of localized sensor arrays used by Body Area Networks (BANs). Prior research has tackled this tradeoff as a coverage problem, wherein a subset of sensors must cover the sensor field. Instead, we formulate it as a power-constrained sampling problem, limiting the number of samples taken per epoch to produce schedules with enhanced coverage and energy savings. This formulation capitalizes on the periodic nature and the strong spatio-temporal interactions that are innate to BAN sensor samples. Our algorithm produces schedules with over 170 % in energy savings with increased sensor coverage that yields up to a 41 % improvement in diagnostic estimates. I.

Abstract—We present a new method for spatiotemporal assignment and scheduling of energy harvesters on a medical shoe tasked with measuring gait diagnostics. While prior work exists on the application of dielectric elastomers (DEs) for energy scavenging onshoes,currentliteraturedoes notaddresstheissues of placement and timing of these harvesters, nor does it address integration into existing sensing systems. We solve these issues and present a self-sustaining medical shoe that harvests energy from human ambulation while simultaneously measuring gait characteristics most relevant to medical diagnosis. I.

Abstract—Recent advances in the scope of wearable devices and networks make body area sensor networks (BASNs) an extremely attractive tool to the fields of mobile and tele-health, owing to the range of medical applications they can serve and the diagnostic richness of patient data they can offer. However, for BASNs to achieve true ubiquity, they must be scalable in their support of automated patient data collection, making usability and reliability key considerations. Its designers must wrestle with the tradeoff be-tween usability, hindered by device intrusiveness into the behav-iors it measures, and lifetime, enhanced by large power supplies and expensive, sturdy components. Furthermore, the validity and reliability of the collected data are paramount. In this paper, we consider these issues in the context of localizedmulti-sensory wear-able networks and present a method to generate low-power sam-pling schedules that are resilient to sensor faults while achieving high diagnostic fidelity. We jointly formulate this as a power-con-strained sampling problem wherein the number of sensors sam-pled per epoch are limited, and, a fault tolerant scheduling problem wherein the sampling scheme offers enough redundancy to endure up to a predefined number of sensor faults while maintaining diag-nostic accuracy. This formulation is based on, 1) the localized scope of BASNs that engenders strong spatio-temporal interactions in the samples, and, 2) the periodic nature of human behaviorsmeasured. We present our algorithm in the context of gait diagnostics derived from a foot plantar pressure measurement platform and illustrate its performance based on real datasets collected by it. Index Terms—Body area sensor networks, energy-efficient sam-pling, fault-tolerant sampling, power-constrained sampling. I.