Smart Device Localization using Femtocell and Macro Base Station Based Path Loss Models in IoT Networks

Pinky, Ankur Pandey, Sudhir Kumar
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引用次数: 2

Abstract

In this paper, a Smart Device (SD) localization method, based on the Path Loss (PL) model of Macro Base Station (MBS) and femtocells, using the convex optimization method is discussed for an Internet of Things (IoT) networks. Localization plays a major role for smart city, smart agriculture, and smart health applications in IoT networks. Global Positioning System (GPS) works well for outdoor positioning but fails to provide accurate locations in an indoor environment and non-line-of-sight (NLOS) paths. We propose the Convex optimization (CO) method that uses the combined effects of the Received Signal Strength (RSS) from macrocells and femtocells. The method requires no additional infrastructure and localizes a Smart Device (SD) in an IoT environment. The Cramèr-Rao Lower Bound (CRLB) is also evaluated to analyze the performance of the estimator. Extensive simulations demonstrate that our proposed method provides an accurate location as compared to Least Square method.
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物联网网络中基于Femtocell和Macro基站的路径损耗模型的智能设备定位
本文讨论了一种基于宏基站(MBS)和飞蜂窝(femtocells)路径损耗(PL)模型的基于凸优化方法的物联网(IoT)网络智能设备(SD)定位方法。在物联网网络中,本地化对智慧城市、智慧农业和智慧健康应用起着重要作用。全球定位系统(GPS)可以很好地用于室外定位,但在室内环境和非视线(NLOS)路径中无法提供准确的位置。我们提出了一种凸优化(CO)方法,该方法利用了来自宏基站和飞基站的接收信号强度(RSS)的综合效应。该方法不需要额外的基础设施,并将智能设备(SD)本地化到物联网环境中。为了分析估计器的性能,还对cram - rao下界(CRLB)进行了评估。大量的仿真表明,与最小二乘法相比,我们提出的方法提供了准确的定位。
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