L N Nguyen Thu, Shin Yoan
School of Electronic Engineering, Soongsil University, Seoul 06978, Korea.
Sensors (Basel). 2019 Jul 13;19(14):3105. doi: 10.3390/s19143105.
Localization is a key-enabling technology for many applications in underwater wireless sensor networks. Traditional approaches for received signal strength (RSS)-based localization often require uniform distribution for anchor nodes and suffer from poor estimates according to unpredictable and uncontrollable noise conditions. In this paper, we establish an RSS-based localization scheme to determine the location of an unknown normal sensor from a certain measurement set of potential anchor nodes. First, we present a practical path loss model for wireless communication in underwater acoustic environments, where anchor nodes are deployed in a random circumstance. For a given area of interest, the RSS data collection is performed dynamically, where the measurement noises and the correlation among them are taken into account. For a pair of transmitter and receiver, we approximate the geometry distance between them according to a linear regression model. Thus, we can obtain a quick access for the range information, while keeping the error, the communication head and the response time low. We also present a method to correct noises in the distance estimate. Simulation results demonstrate that our localization scheme achieves a better performance for certain scenario settings. The successful localization probability can be up to 90%, where the anchor rate is fixed at 10%.
定位是水下无线传感器网络中许多应用的关键支撑技术。基于接收信号强度（RSS）的传统定位方法通常要求锚节点均匀分布，并且在不可预测和不可控的噪声条件下估计效果较差。在本文中，我们建立了一种基于RSS的定位方案，以从潜在锚节点的特定测量集中确定未知普通传感器的位置。首先，我们提出了一种用于水下声学环境中无线通信的实用路径损耗模型，其中锚节点是在随机情况下部署的。对于给定的感兴趣区域，动态执行RSS数据收集，其中考虑了测量噪声及其之间的相关性。对于一对发射器和接收器，我们根据线性回归模型近似它们之间的几何距离。因此，我们可以快速获取距离信息，同时保持误差、通信开销和响应时间较低。我们还提出了一种校正距离估计中噪声的方法。仿真结果表明，我们的定位方案在某些场景设置下具有更好的性能。在锚节点率固定为10%的情况下，成功定位概率可达90%。
