RSS Estimation Based on Bayesian Learning Mechanism by Vehicular Sensor Networks

Silan Zheng, Cailian Chen, X. Guan, Li Yu
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Abstract

Received Signal Strength (RSS) estimation for networks in urban transportation systems can be carried out by Vehicular Sensor Networks (VSN). All moving vehicles on roads with signal-sensing applications can act as mobile sensors, collecting RSS along their driving routes and uploading data to data center at the end of every drive. However, there must be inconsistencies among RSS values achieved at same locations owing to different types and brands for vehicles. In this paper, we propose a RSS estimation algorithm to improve the credibility of RSS information at certain locations. This algorithm is based on Bayesian learning mechanism and calibrated by the values gained by high-precision equipment PXI experimental platform. We evaluate the algorithm based on the real-world data collected by our Android application. The results demonstrate the effectiveness and superiority of the method compared with typical algorithms.
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基于贝叶斯学习机制的车载传感器网络RSS估计
城市交通系统网络的接收信号强度(RSS)估计可以通过车辆传感器网络(VSN)来实现。所有安装了信号传感应用程序的行驶车辆都可以充当移动传感器,沿着行驶路线收集RSS,并在每次行驶结束时将数据上传到数据中心。但是,由于车辆的类型和品牌不同,在同一地点取得的RSS值必然不一致。在本文中,我们提出了一种RSS估计算法,以提高RSS信息在特定位置的可信度。该算法基于贝叶斯学习机制,并通过高精度仪器PXI实验平台获得的数值进行校准。我们基于Android应用程序收集的真实数据来评估算法。结果表明,该方法与典型算法相比具有较好的有效性和优越性。
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