College of Computer Science, Zhejiang University, Hangzhou 310027, P.R. China; E-Mails:
Sensors (Basel). 2009;9(4):2760-79. doi: 10.3390/s90402760. Epub 2009 Apr 20.
The ability to automatically locate sensor nodes is essential in many Wireless Sensor Network (WSN) applications. To reduce the number of beacons, many mobile-assisted approaches have been proposed. Current mobile-assisted approaches for localization require special hardware or belong to centralized localization algorithms involving some deterministic approaches due to the fact that they explicitly consider the impreciseness of location estimates. In this paper, we first propose a range-free, distributed and probabilistic Mobile Beacon-assisted Localization (MBL) approach for static WSNs. Then, we propose another approach based on MBL, called Adapting MBL (A-MBL), to increase the efficiency and accuracy of MBL by adapting the size of sample sets and the parameter of the dynamic model during the estimation process. Evaluation results show that the accuracy of MBL and A-MBL outperform both Mobile and Static sensor network Localization (MSL) and Arrival and Departure Overlap (ADO) when both of them use only a single mobile beacon for localization in static WSNs.
在许多无线传感器网络(WSN)应用中,自动定位传感器节点的能力至关重要。为了减少信标数量,已经提出了许多移动辅助方法。由于当前的移动辅助定位方法明确考虑了位置估计的不准确性,因此需要特殊的硬件或属于集中式定位算法,涉及一些确定性方法。在本文中,我们首先为静态 WSN 提出了一种无范围、分布式和概率性的移动信标辅助定位(MBL)方法。然后,我们提出了另一种基于 MBL 的方法,称为自适应 MBL(A-MBL),通过在估计过程中自适应调整样本集的大小和动态模型的参数,来提高 MBL 的效率和准确性。评估结果表明,在静态 WSN 中,仅使用单个移动信标进行定位时,MBL 和 A-MBL 的准确性均优于移动和静态传感器网络定位(MSL)和到达和离开重叠(ADO)。