An Interpolation Algorithm Based on Sliding Neighborhood in Wireless Sensor Networks

There are usually coverage holes caused by uneven deployment of the nodes in wireless sensor network, which brings about many difficulties in various applications, as data value in coverage holes can’t be detected directly. To solve this problem, the major idea so far is to place new nodes into the coverage holes with respect to the coverage percentage. However, it is hard to cover the whole region by deploying new nodes, when the network size is huge. Recently, a few methods are designed to estimate the data value in the coverage hole using sensed data of neighboring nodes. Motivated by this idea, a new moving-neighborhood interpolation algorithm is proposed in this paper. The algorithm based on Delaunay triangulation technique can find out a neighbor set of points by iterative searching, which has strong spatial correlation with interpolation point. Then, the value of interpolation point is able to estimate by our proposed algorithm, according to the observations of neighbor set. Moreover, an adaptive selection technique is designed for searching different neighbor set to meet different error thresholds. Experimental results on a real-world dataset show that our proposed algorithm can estimate the value of coverage holes more accurately and robustly. Besides, it can help retrieve the real-time value of a point in the sensor network.