Approximate Data Collection Using Resolution Control Based On Context

Approximate data collection is an important mechanism for real-time and high sampling rate monitoring applications in body sensor networks, especially when there are multiple sensor sources. Unlike traditional approaches that utilize temporal or spatio-temporal correlations among the measurements of the multiple sensors observing a physical process to reduce the communication cost, in this paper we explore the idea of assigning different context-dependent priorities to the various sensors, and allocating communication resources according to data from a sensor according to its priorities. Specifically, a higher number of bits per sample is allocated to sensors that are of higher priority in the current context. We demonstrate that the proposed approach provides accurate inference results while effectively reducing the communication load.