Model-driven adaptive wireless sensing for environmental healthcare feedback systems

While the connectivity, sensing, and computational capabilities of today's smartphones have increased, congestion in wireless channels and energy consumption remain major issues. We present a technique for model-driven adaptive environmental sensing, designed to reduce the amount of data that is communicated over the cellular network. In simulations of an exposure monitoring system, our technique reduced the number of messages sent by 85%, obtained power savings of 80% while generating a global model of pollution with error of maximum 0.5 ppm, a negligible amount for the application of interest.