Efficient Data Harvesting for Tracing Phenomena in Sensor Networks

Many publish/subscribe systems have been built using wireless sensor networks, WSNs, deployed for real-world environmental data collection, security monitoring, and object tracking. However, research efforts on WSN-based publish/subscribe systems have largely focused on routing algorithms leaving data management issues mostly untouched. This paper considers a publish/subscribe system built on top of a sensor network that monitors the occurrences of phenomena. In quest for explanations to the occurrence of a phenomenon, a subscriber poses one-time queries to the sensor network for sensor readings taken seconds or minutes before the reported phenomenon occurred. These types of queries cannot be satisfied by subscriptions since subscriptions are only effective in delivering streams of new phenomena. To efficiently answer such queries, it is imperative that a data farm of sensor readings be cultivated within WSNs. This paper proposes a new algorithm for archiving sensor readings on data farm that leverages the non-volatile memory of sensor nodes in the network. The proposed algorithm takes advantage of the memory space on nodes that have low probabilities of detecting phenomena. By running an extensive set of simulation experiments, the performance results show that the proposed algorithm can provide 32.9% memory gain and 81.8% low communication overhead when compared to an approach in which nodes use only their own physical memory