K Nearest Neighbour Query Processing in Wireless Sensor and Robot Networks

The k-Nearest Neighbors (kNN) query is an important spatial query in mobile sensor networks. In this work we extend kNN to include a distance constraint, calling it a l-distant k-nearest-neighbors (l-kNN) query, which finds the k sensor nodes nearest to a query point that are also at l or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l- kNN query can be used in most kNN applications for the case of well distributed query results. To process an l-kNN query, we must discover all sets of kNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-kNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l- kNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting k sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l- kNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the kNN query in terms of energy efficiency, query latency, and accuracy.

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“…Many studies have proposed ways to optimize the performance of the

NN query in various environments or conditions [ 17 , 18 ]. Xie et al [ 17 ] proposed

NN query processing method in wireless and robot networks (WSRNs). Huang at al.…”

Section: Related Workmentioning

confidence: 99%