传感器网络中节点能量有效均衡的Top-k查询技术

摘要

Energy efficiency and balance of sensor nodes in processing top-k queries can prolong the lifetime of wireless sensor networks. In this paper, an Energy-efficient and Balanced query Sampling Top-k algorithm named EBSTop k(,) is proposed, which is based on the sampling techniques and the spatial correlations among sensor nodes. First, the sensor network is partitioned into several regions. Next, the linear regression prediction model and Gaussian prediction model are constructed based on the spatial correlations of pairwise sensor nodes. Then, the criteria of high spatial correlation is established due to the given relative error bound and the confidence level 1. Finally, according to the predicting models and criteria above, two energy balanced algorithms named EBSTopk(,)-LR and EBSTopk(,)-MG are proposed, which are based on iterative random sampling technique. Experimental results show that, the proposed EBSTopk(,) algorithms not only reduce the global energy consumption in wireless sensor networks, but also achieve balanced energy consumption among all sensor nodes after continuous processing top-k queries.%无线传感器网络中 top-k 查询处理的节点能量高效以及实现各节点的能量消耗均衡，可以有效延长网络的生命周期。该文提出一种基于采样技术和节点空间相关性，来实现节点的能量均衡和高效的查询处理算法，称为能量均衡采样(，)近似top-k算法EBSTop k(，)。首先对传感器网络进行分区处理，利用区域内两两节点间的空间相关性对其建立线性回归预测模型和高斯预测模型；然后根据用户给定的相对误差界和置信水平1建立节点高相关性预测准则；最后根据上述预测模型和准则，提出基于反复随机采样的能量均衡算法EBSTop k(，)-LR和EBSTop k(，)-MG。实验表明，所提出的EBSTop k(，)算法减少了无线传感器网络中的全局能量消耗，且在多次top-k查询后各节点的能量消耗达到均衡。