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.
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