A Distortion-Aware Scheduling Approach for Wireless Sensor Networks”, DCOSS

Abstract. An important class of applications for wireless sensor networks is to use the sensors to provide samples of a physical phenomenon at discrete locations. Through interpolation-based reconstruction, a continuous map of the monitored environment can be built. In this paper, we leverage the spatial correlation characteristics of the physical phenomenon and find the minimum set of nodes that needs to be active at each point in time for a sufficiently accurate reconstruction. Furthermore, multiple such sets of nodes are found so that a different set can report at each point in time in a rotating fashion. This is crucial in improving network lifetime. To perform all related scheduling tasks we employ a novel approach which does not assume a-priori knowledge of the underlying phenomenon. Instead it jointly estimates process characteristics and performs node selection online. We illustrate that significant gains in network lifetime can be achieved with minimal impact on the overall reconstruction quality, measured in terms of distortion