Estimating distances via received signal strength and connectivity in wireless sensor networks
Estimating distances via received signal strength and connectivity in wireless sensor networks
复制标题
通过无线传感器网络中接收信号强度和连接性估计距离
DOI：
10.1007/s11276-018-1843-8
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发表时间：
2020-02-01
影响因子：
3
通讯作者：
Jia, Bing
中科院分区：
文献类型：
--
作者：
Miao, Qing;Huang, Baoqi;Jia, Bing
距离估计对于无线传感器网络（WSNs）的定位和许多其他应用至关重要。特别是，在低成本无线传感器网络中，不需要使用特定的硬件就可以实现距离估计和定位。因此，基于接收信号强度（RSS）的方法和基于连通性的方法都受到了广泛的关注。基于RSS的方法适用于短距离估计，而基于连通性的方法在长距离估计中获得相对较好的性能。考虑到这两种方法的互补性，我们提出了一种基于极大似然估计的融合方法，通过有效地融合来自RSS和本地连通性的信息来估计WSN中任意对相邻节点之间的距离。此外，本文还在实际对数正态阴影模型下报道了该方法，并推导了相关的Cramer-Rao下界（CRLB）用于性能分析。仿真和基于实际测量的实验表明，该方法优于任何单一方法和CRLB方法。
Distance estimation is vital for localization and many other applications in wireless sensor networks (WSNs). Particularly, it is desirable to implement distance estimation as well as localization without using specific hardware in low-cost WSNs. As such, both the received signal strength (RSS) based approach and the connectivity based approach have gained much attention. The RSS based approach is suitable for estimating short distances, whereas the connectivity based approach obtains relatively good performance for estimating long distances. Considering the complementary features of these two approaches, we propose a fusion method based on the maximum-likelihood estimator to estimate the distance between any pair of neighboring nodes in a WSN through efficiently fusing the information from the RSS and local connectivity. Additionally, the method is reported under the practical log-normal shadowing model, and the associated Cramer–Rao lower bound (CRLB) is also derived for performance analysis. Both simulations and experiments based on practical measurements are carried out, and demonstrate that the proposed method outperforms any single approach and approaches to the CRLB as well.