Distributed clustering algorithm for spatial field reconstruction in wireless sensor networks

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In this paper, we consider the problem of distributed spatial estimation for field reconstruction in wireless sensor networks. In order to estimate the field, a geostatistical technique called kriging is used. Centralized spatial estimation algorithms with a large number of sensors lead to significant computational cost and energy wastage. We present a novel distributed clustering algorithm for estimating spatial interference maps, which are essential for operations and management in future wireless networks. In this algorithm, clusters are adaptively formed with a small subset of sensors by minimizing the kriging variance. The semivariogram computation and kriging prediction are locally performed in each cluster in a distributed fashion. The complexity of the clustering algorithm is analyzed and its performance is evaluated by comparing it with centralized and other distributed approaches.
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V. Chowdappa, C. Botella and B. Beferull-Lozano, "Distributed Clustering Algorithm for Spatial Field Reconstruction in Wireless Sensor Networks," 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 2015, pp. 1-6