Adaptive Data Quality for Persistent Queries in Sensor Networks

Wireless sensor networks are emerging as a convenient mechanism to constantly monitor the physical world. The volume of information in such networks can be extremely large and, to be meaningful to applications, this information must be processed at the right level of accuracy. However, there is an inherent trade-off between achieving a high degree of data accuracy and the communication overhead associated with achieving it. We present a simple mechanism for spatially approximate query processing. We present a protocol that leverages gossip based routing to collect network data from a randomly selected set of nodes at a user-defined level of accuracy. We extend this protocol to address persistent queries, long running queries where network data is collected periodically, by treating a persistent query as a temporal aggregate of individual queries. Finally, we provide a novel protocol that dynamically adapts its accuracy based on the quality of the responses to individual requests in the persistent query. We describe this protocol in detail and evaluate its performance through simulation