Adaptive processing of historical spatial range queries in peer-to-peer sensor networks

Abstract We investigate the problem of processing historical queries on a sensor network. Since data is considered to have been already collected at the sensor nodes, the main issue is exploring the spatial component of the query in order to minimize its cost represented by the energy consumption. We assume queries can be issued at any network node, i.e., there is no central base station and all nodes have only local knowledge of the network. On the one hand, a globally optimum query processing plan is desirable but its construction is not possible due to the lack of global knowledge of the network. On the other hand, while a simple network flooding is feasible, it is not a practical choice from a cost perspective. To address this problem we propose a two-phase query processing strategy, where in the first phase a path from the query originator to the query region is found and in the second phase the query is processed within the query region itself. This strategy is supported by analytical models that are used to dynamically select the best processing strategy depending on the query specifics. Our extensive analytical and experimental results show that our analytical models are accurate and that the two-phase strategy is better suited for small to medium sized queries, being up to 10 times more cost effective than a typical network flooding. In addition, the dynamic selection of a query processing technique proved itself capable of always delivering at least as good performance as the most energy efficient strategy for all query sizes.