Opportunistic sampling-based query processing in wireless sensor networks
High resolution sampling of physical phenomenon is a prime application of large scale wireless sensor networks (WSNs). With hundreds of nodes deployed over vast tracts of land, monitoring data can now be generated at unprecedented spatio-temporal scales. However, the limited battery life of individual nodes in the network mandates smart ways of collecting this data by maximizing localized processing of information at the node level. In this paper, we propose a WSN query processing method that enhances localized information processing by harnessing the two inherent aspects of WSN communication, i.e., multihop and multipath data transmission. In an active WSN where data collection queries are regularly processed, multihop and multipath routing leads to a situation where a significant proportion of nodes relay and overhear data generated by other nodes in the network. We propose that nodes opportunistically sample this data as they communicate. We model the data communication process in a WSN and show that opportunistic sampling during data communication leads to surprisingly accurate global knowledge at each node. We present an opportunistic query processing system that uses the accumulated global knowledge to limit the data collection requirements for future queries while ensuring temporal freshness of the results.
query processing wireless sensor networks spatio-temporal modeling