文件名称:Fusion-based-Sensor-Placement
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论文 在使用无线传感器网络进行目标检测时,如何布置尽可能少的传感器节点而同时实现高的正确检测概率和
低的误警率,是关键问题之一。采用数据融合技术,能实现传感器节点之间的协同,从而大幅提高目标检测精度。提
出了用于目标检测的精度模型,分析了数据融合半径与传感器节点密度之间的关系,设计聚类方法将目标点组织成布
置单元,从高密度单元到低密度单元布置传感器节点覆盖目标区域。仿真结果表明,算法在保证检测精度的同时能有
效减少所使用的传感器节点数目。
-
Sensor placement is a key issue in target detection,placing smaller sensors to achieve high detection accuracy
which presented as a specific high detection probability and a low false alarm rate is very important.Data fusion based
collaboration between sensors can boost the level of accuracy in general.In this paper,the accuracy model and the rela-
tionship between the fusion radius and density of sensors were analyzed a quality-threshold clustering algorithm was
employed to organize the surveillance locations into placement units,place sensors in them from the intensive to sparse
one.Simulation results show that this placement algorithm can significantly reduce the total number of sensors with
guaranteed accuracy.
低的误警率,是关键问题之一。采用数据融合技术,能实现传感器节点之间的协同,从而大幅提高目标检测精度。提
出了用于目标检测的精度模型,分析了数据融合半径与传感器节点密度之间的关系,设计聚类方法将目标点组织成布
置单元,从高密度单元到低密度单元布置传感器节点覆盖目标区域。仿真结果表明,算法在保证检测精度的同时能有
效减少所使用的传感器节点数目。
-
Sensor placement is a key issue in target detection,placing smaller sensors to achieve high detection accuracy
which presented as a specific high detection probability and a low false alarm rate is very important.Data fusion based
collaboration between sensors can boost the level of accuracy in general.In this paper,the accuracy model and the rela-
tionship between the fusion radius and density of sensors were analyzed a quality-threshold clustering algorithm was
employed to organize the surveillance locations into placement units,place sensors in them from the intensive to sparse
one.Simulation results show that this placement algorithm can significantly reduce the total number of sensors with
guaranteed accuracy.
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Fusion-based Sensor Placement.caj