Spatio-temporal range searching over compressed kinetic sensor data

As sensor networks increase in size and number, efficient techniques are required to process the very large data sets that they generate. Frequently, sensor networks monitor objects in motion within their vicinity; the data associated with the movement of these objects are known as kinetic data. In an earlier paper we introduced an algorithm which, given a set of sensor observations, losslessly compresses this data to a size that is within a constant factor of the asymptotically optimal joint entropy bound. In this paper we present an efficient algorithm for answering spatio-temporal range queries. Our algorithm operates on a compressed representation of the data, without the need to decompress it. We analyze the efficiency of our algorithm in terms of a natural measure of information content, the joint entropy of the sensor outputs. We show that with space roughly equal to entropy, queries can be answered in time that is roughly logarithmic in entropy. In addition, we show experimentally that on real-world data our range searching structures use less space and have faster query times than the naive versions. These results represent the first solutions to range searching problems over compressed kinetic sensor data.