Reliable estimation of influence fields for classification and tracking in unreliable sensor networks

The influence field of an object, a commonly exploited feature in science and engineering applications, is the region where the object is detectable by a given sensing modality. Being spatially distributed, this feature allows us to tradeoff nodal computation with network communication. By the same token, not only is its calculation subject to nodal failures and false detections, but also to channel fading and channel contention. In this paper, we study how to accurately and efficiently estimate the influence fields of objects in such an unreliable setting and how this reliable estimation of influence fields can be used to classify and track different types of objects. We derive, for node and network fault models, the necessary nodal density for reliably estimating the influence fields so that objects can be classified and tracked. We present four algorithmic techniques: temporal aggregation, probabilistic reporting, temporal segregation and spatial reconstruction, to deal with cases where the effective network density differs from this minimum. We provide corroboration of our analysis through field experiments with Mica2 sensor nodes wherever appropriate. Finally, we demonstrate how these results and techniques were applied to achieve reliable and efficient classification and tracking in a fielded system of 90 Mica2 sensor nodes that we called "A Line In The Sand\´.