Data-Driven Processing in Sensor Networks

ABSTRACT Wireless sensor networks are poised to enable continuous data col- lection on unprecedented scales, in terms of area location and size, and frequency. This is a great boon to elds,such as ecological modeling. We are collaborating with researchers to build sophisti- cated temporal and spatial models of forest growth, utilizing a va- riety of measurements. There exists a crucial challenge in support- ing this activity: network nodes have limited battery life, and radio communication,is the dominant,energy consumer. The straightfor- ward solution of instructing all nodes to report their measurements as they are taken to a base station will quickly consume,the net- work’s energy. On the other hand, the solution of building mod- els for node behavior and substituting these in place of the actual measurements,is in conict,with the end goal of constructing mod- els. To address this dilemma, we propose data-driven processing, the goal of which is to provide continuous data without continu- ous reporting, but with checks against the actual data. Our primary strategy for this is suppression, which uses in-network monitoring to limit the amount,of communication,to the base station. Sup- pression employs models for optimization of data collection, but not at the risk of correctness. We discuss techniques for designing data-driven collection, such as building suppression schemes and incorporating models into them. We then present and address some of the major challenges to making this approach practical, such as handling failure and avoiding the need to co-design the network application and communication,layers.