Detecting change in an environment with wireless sensor networks

© 2010 Dr. Mingzheng ShiThis thesis is motivated by a new observation tool, called wireless sensor network (WSN). WSN, as any other observation method, must cope with spatial, temporal, and thematic granularities of observations. This thesis investigates how WSNs can efficiently detect different types of changes of spatial phenomena, or objects. Two types of changes, i.e., gradual and abrupt spatial changes, are distinguished based on the quantity of change in particular, WSN inherent granularities. A new spatiotemporal data model is proposed for the representation of dynamic spatial objects in WSNs. An algorithm is then designed for WSNs to detect both gradual and abrupt spatial changes with different spatial, temporal, and thematic granularities. The efficiency of the algorithm is proven by qualitative and quantitative evaluation in WSN simulations. This thesis also proposes a new network structure, called multi-granularity sensor network, for different granularities of observations