A key challenge facing many applications of new geosensor networks technology is to derive meaningful spatial knowledge from low-level sensed data. This paper presents a formal model for representing and computing topological relationship changes between continuously evolving regions monitored by a geosensor network. The definition of "continuity" is used to constrain region evolution and enables the local detection of node state transitions in the network. The model provides a computational framework for the detection of global high-level qualitative relationship changes from local low-level quantitative sensor measurements. In this paper, an efficient decentralized algorithm is also designed and implemented to detect relationship changes and its computational efficiency is evaluated experimentally using simulation.
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