The κ -Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors ( l-κNN) query, which finds the κ sensor nodes
[...] Read more.
The κ -Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l
-distant κ-nearest-neighbors ( l
-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at 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
-κNN query can be used in most κNN applications for the case of well distributed query results. To process an l
-κNN query, we must discover all sets of κNN 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
-κNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l
-κNN 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 κ sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l
-κNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the κNN query in terms of energy efficiency, query latency, and accuracy.