传感器网络中基于卡尔曼滤波的能量高效Top-k查询处理技术

Energy-Efficient Top-k Query Processing Techniques Based on Kalman Filter in Wireless Sensor Networks

无线传感器网络能量受限,如何实现top-k查询处理的能量高效从而延长网络的生命周期是该领域研究的一个重要课题。论文利用传感器节点读数的时空相关性,提出运用卡尔曼滤波根据已知节点读数对未知节点读数估计的时空建模方法,进而提出基于预测机制的区域采样方法(Region Sampling,RS)。实验表明,论文提出的查询方法不但可以满足用户的查询精度要求,而且大大减少了传感器网络的通信次数节省了能量,从而延长了网络的生命周期。

Processing top-k queries in an energy-efficient manner is an important and challenging topic in energy-limited wireless sensor networks. In this paper, a spatio-emporal modeling method based on Kalman filter is proposed according to the spatiotemporal correlations a mong sensor readings, in which only several sensors need to be sampled and the values of other non sampled sensors can be estimated by the sampled sensor readings. After that, region sampling approach named as RS is proposed. The experimental results show that the proposed query processing techniques not only satisfy the precision requests, but also reduce the radio communications and energy consumption, whic will prolong the lifetiome of the wireless sensor networks.