This paper presents a filter-based algorithm called PREDICTOR for optimizing multidimensional K-NN queries in WSN. A filter installed at each sensor node is a node value distribution range. It is used to prevent the node from sending the data that belongs to the covering range of the filter, and so the node's energy is saved. The server keeps the historical sample data of all the nodes, and determines filters for them according to the query requirement and the samples. Three optimization strategies are proposed: (1) the method for dynamically adjusting the assignment strategies of the filter covering ranges so that a node with little chance to contribute to the final result is assigned with a larger covering range; (2) the method for sharing the filter among nodes so that the nodes with similar historical data are assigned with the same filter; (3) the method for transmitting filters in a compressed way to reduce the cost of filter updating for different K-NN queries. Evaluation experiment results prove the efficiency of the algorithm PREDICTOR in energy saving. Compared with the naive method, this approach can reduce the transmission volume dramatically.%提出了一种基于过滤器的无线传感器网络多维K-NN查询优化算法PREDICTOR.过滤器是设置在节点端的取值分布区间,用来屏蔽节点发送属于区间内的数据,从而节省节点能耗.在服务器端保存有各节点的历史样本数据,根据K-NN查询请求和样本数据的分布范围为节点定义过滤器.提出了3种优化策略:(1) 过滤器覆盖区间大小分配策略的动态调整方法,使得进入最终查询结果可能性小的节点拥有较大的覆盖区间;(2) 节点间过滤器共享方法,使得历史样本数据相近的节点使用相同的过滤器;(3) 过滤器压缩传输方法,减少为不同K-NN查询更新过滤器的代价.通过实验评价,验证了PREDICTOR算法的能量有效性,与朴素算法相比,极大地降低了数据传输量.
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