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在无线传感网络中,节点定位是基于位置的应用基本要求。然而,现多数文献仅关注定位精度,而忽略了能量消耗对定位精度的影响。为此,针对基于接收信号强度 RSSI(Received Signal Strength Indicator)定位方案,提出基于平方位置误差下限 SPEB (Squared Position Error Bound)的优化功率分配 SPEB-OPA(SPEB-Based Optimal Power Allocation)方案,目的在于最小化能量消耗。在 SPEB-OPA 算法中,将 SPEB 作为评定定位精度的参数,并推导出 SPEB 表达式,然后建立优化功率分配的目标函数,并考虑到锚节点位置存在误差。仿真结果表明,提出的 SPEB-OPA 方案极大地减少了功率消耗。当误差门限 T =8时,SPEB-OPA 方案的功率消耗比统一功率分配 UPA(Uniform Power allocation)方案减少至50%。  相似文献   
2.
为了减少无线传感器网络节点的定位误差,提出一种人工蜂群算法(ABC)修正最小二乘(LS)定位误差的传感器节点定位算法( ABC-LS). 首先估计未知传感器节点与信标节点间距离,然后采用LS算法初步确定未知传感器节点位置,最后采用ABC算法对LS算法的节点定位误差进行修正,并采用仿真实验测试ABC-LS与其他节点定位算法的优劣. 结果表明,ABC-LS提高了无线传感器节点的定位精度.  相似文献   
3.
Location information of sensor nodes is of vital importance for most applications in wireless sensor networks (WSNs). This paper proposes a new range-free localisation algorithm using support vector machine (SVM) and polar coordinate system (PCS), LSVM-PCS. In LSVM-PCS, two sets of classes are first constructed based on sensor nodes’ polar coordinates. Using the boundaries of the defined classes, the operation region of WSN field is partitioned into a finite number of polar grids. Each sensor node can be localised into one of the polar grids by executing two localisation algorithms that are developed on the basis of SVM classification. The centre of the resident polar grid is then estimated as the location of the sensor node. In addition, a two-hop mass-spring optimisation (THMSO) is also proposed to further improve the localisation accuracy of LSVM-PCS. In THMSO, both neighbourhood information and non-neighbourhood information are used to refine the sensor node location. The results obtained verify that the proposed algorithm provides a significant improvement over existing localisation methods.  相似文献   
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