电子与信息学报
電子與信息學報
전자여신식학보
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
2014年
6期
1478-1484
,共7页
无线传感器网络%能量均衡%近似top-k查询%反复随机采样
無線傳感器網絡%能量均衡%近似top-k查詢%反複隨機採樣
무선전감기망락%능량균형%근사top-k사순%반복수궤채양
Wireless sensor networks%Energy balance%Approximate top-k query%Iterative random sampling
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。
無線傳感器網絡中 top-k 查詢處理的節點能量高效以及實現各節點的能量消耗均衡,可以有效延長網絡的生命週期。該文提齣一種基于採樣技術和節點空間相關性,來實現節點的能量均衡和高效的查詢處理算法,稱為能量均衡採樣(?,?)近似top-k算法EBSTop k(?,?)。首先對傳感器網絡進行分區處理,利用區域內兩兩節點間的空間相關性對其建立線性迴歸預測模型和高斯預測模型;然後根據用戶給定的相對誤差界?和置信水平1??建立節點高相關性預測準則;最後根據上述預測模型和準則,提齣基于反複隨機採樣的能量均衡算法EBSTop k(?,?)-LR和EBSTop k(?,?)-MG。實驗錶明,所提齣的EBSTop k(?,?)算法減少瞭無線傳感器網絡中的全跼能量消耗,且在多次top-k查詢後各節點的能量消耗達到均衡。
무선전감기망락중 top-k 사순처리적절점능량고효이급실현각절점적능량소모균형,가이유효연장망락적생명주기。해문제출일충기우채양기술화절점공간상관성,래실현절점적능량균형화고효적사순처리산법,칭위능량균형채양(?,?)근사top-k산법EBSTop k(?,?)。수선대전감기망락진행분구처리,이용구역내량량절점간적공간상관성대기건립선성회귀예측모형화고사예측모형;연후근거용호급정적상대오차계?화치신수평1??건립절점고상관성예측준칙;최후근거상술예측모형화준칙,제출기우반복수궤채양적능량균형산법EBSTop k(?,?)-LR화EBSTop k(?,?)-MG。실험표명,소제출적EBSTop k(?,?)산법감소료무선전감기망락중적전국능량소모,차재다차top-k사순후각절점적능량소모체도균형。
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.