Efficient long-term quality-of-inference (QoINF)-aware context determination in pervasive care environments

Energy-efficient determination of an individual's context (both physiological and activity) is an important technical challenge for assisted living environments. Given the expected availability of multiple sensors, context determination may be viewed as an estimation problem over multiple sensor data streams. This paper develops a formal, and practically applicable, model to capture the tradeoff between the accuracy of context estimation and the communication overheads of sensing. In particular, we propose the use of tolerance ranges to reduce an individual sensor's reporting frequency, while ensuring acceptable accuracy of the derived context. In our vision, applications specify their minimally acceptable value for a Quality-of Inference (QoINF) metric. We introduce an optimization technique allowing the Context Service to compute both the best set of sensors, and their associated tolerance values, that satisfy the QoINF target at minimum communication cost. Early experimental results with SunSPOT sensors are presented to attest to the promise of this approach.