Low Cost Region Detection from Distributed Sensor Observations

The applications of wireless sensor networks often require region representations of the phenomena that the networks monitor for visualization and interaction with spatial databases. However, there is a fundamental gap between the point-based sensor observation of non-regular sensor deployment and the spatio-temporal phenomena represented by regions. This paper proposes to bridge the gap by providing low-communication-cost approaches to create regions from the distributed sensors. The solution is based on Spatial Grid partition and the task is distributed among a set of sensors called ``cell leaders''. We formalize the problem of cell-leader selection with the objective of minimizing sensor communication cost. We propose two heuristic algorithms, i.e., Greedy Lattice Search and Subregion Centroid Approximation. We also propose Energy-Aware Local Update model to allow the dynamic re-assignment of cell leaders. The experiments performed on a real sensor observation dataset demonstrated that our heuristic approaches can achieve much better communication cost compared with the baseline methods.