Processing Spatiotemporal Data Map Queries with Redundancy Removal in Sensor Networks

Wireless sensor networks are made of autonomous devices that are able to collect information, store it, process it and share it with other devices. Such framework can be used to eciently query spatiotemporal data, e.g., for monitoring humidity and temperature levels across a wide geographical region. Typical spatiotemporal region queries require the answers of only the subset of the network nodes that fall into the spatial area of the query. If the network is redundant in the sense that nodes' measurements can be substituted by those of other nodes with a certain degree of confidence, then only a much smaller subset of nodes may be sucient to answer the query at a much lower energy cost. In this paper we investigate how to take advantage of such data redundancy, and we propose three techniques to process spatiotemporal region queries under these conditions. We show, through extensive experimentation, that taking advantage of the data redundancy reduces up to twenty times the energy-cost of query processing, thus prolonging the sensor networks lifetime.