SDR based indoor localization using ambient WiFi and GSM signals

Nambiar, Varun; Vattapparamban, Edwin; Yurekli, Ali Ihsan; Güvenç, İsmail; Mozaffari, Mohammad; Saad, Walid

doi:10.1109/iccnc.2017.7876262

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…This data set shows BLE related applications such as home automation [179][180][181] and indoor location [182][183][184] and health care [185][186][187]. WiFi is the other network protocol used for IoT research, with a total of 85 publications, and applications related to: home automation [188,189], indoor localization [190]. Nevertheless, with this wireless technology, some authors have focused on how the 2.4 GHz spectrum could be efficiently used with other IoT network protocols [191][192][193][194][195][196][197].…”

Section: Communication Protocols According To Open Systems Interconnementioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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Abstract:Internet of Things (IoT) is connecting billions of devices to the Internet. These IoT devices chain sensing, computation, and communication techniques, which facilitates remote data collection and analysis. wireless sensor networks (WSN) connect sensing devices together on a local network, thereby eliminating wires, which generate a large number of samples, creating a big data challenge. This IoT paradigm has gained traction in recent years, yielding extensive research from an increasing variety of perspectives, including scientific reviews. These reviews cover surveys related to IoT vision, enabling technologies, applications, key features, co-word and cluster analysis, and future directions. Nevertheless, we lack an IoT scientometrics review that uses scientific databases to perform a quantitative analysis. This paper develops a scientometric review about IoT over a data set of 19,035 documents published over a period of 15 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016) in two main scientific databases (Clarivate Web of Science and Scopus). A Python script called ScientoPy was developed to perform quantitative analysis of this data set. This provides insight into research trends by investigating a lead author's country affiliation, most published authors, top research applications, communication protocols, software processing, hardware, operating systems, and trending topics. Furthermore, we evaluate the top trending IoT topics and the popular hardware and software platforms that are used to research these trends.

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Section: Communication Protocols According To Open Systems Interconnementioning

confidence: 99%

Internet of Things: A Scientometric Review

et al. 2017

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“…21 The solution for the three-dimensional case was the subject of the investigation in Kuang 22 and Pollefeys and Nister. 23 The authors provided a non-iterative solution for the cases with (5,5), (6,4), and (10,4) transponders and receivers. The roles of the base stations and the transponders are equivalent; hence, it does not matter if six base stations and four transponders are used or vice versa.…”

Section: Introductionmentioning

confidence: 99%

“…In position estimation the Time of Arrival (ToA) [2] technique is standard. The area of application extends from satellite based systems like GPS [11], GLONASS [8], Galileo [6], mobile phone localization (GSM) [14], radar based systems such as UWB [17], FMCW radar [22] to acoustic systems [3]. The ToA technique leads to a quadratic equation.…”

Section: Introductionmentioning

confidence: 99%

Improved Time of Arrival measurement model for non‐convex optimization

et al. 2019

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The quadratic system provided by the Time of Arrival technique can be solved analytically or by nonlinear least squares minimization. An important problem in quadratic optimization is the possible convergence to a local minimum, instead of the global minimum. This problem does not occur for Global Navigation Satellite Systems (GNSS), due to the known satellite positions. In applications with unknown positions of the reference stations, such as indoor localization with self-calibration, local minima are an important issue. This article presents an approach showing how this risk can be significantly reduced. The

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“…22,23 A combination of wireless technologies can be used to improve localization accuracy. 24 RF fingerprinting can be carried out using received signal features, in particular signal strength, such as RSSI, reference signal received power (RSRP), reference signal received quality (RSRQ) and CFR/CSI or equivalently the channel impulse response (CIR). 4,6,25,26 Time of arrival (TOA), timing advancing (TA), time difference of arrival (TDOA) and angle of arrival (AOA) are less frequently used in fingerprintingbased approaches.…”

Section: Related Workmentioning

confidence: 99%

A novel indoor localization method using passive phase difference fingerprinting based on channel state information

Dang

Ren

Hao

et al. 2019

International Journal of Distributed Sensor Networks

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The device-free channel state information indoor fingerprint localization method may lead to phase offset errors, strong fingerprint noise and low sampling classification accuracy. In light of these characteristics, this article presents an indoor localization algorithm that is based on phase difference processing and principal component analysis. First, during the offline phase, this algorithm calculates phase differences to correct for random phase shifts and random time shifts in communication links. Second, the principal component analysis method is used to reduce the dimensionality of the denoised data and establish a robust fingerprint database. During the online phase, the algorithm trains a back-propagation neural network using the fingerprint data and determines the modelled mapping relationship between the fingerprint data and the physical localization after carrying out the phase difference correction and the principal component analysis-based dimensionality reduction. The experiments show that compared with existing fingerprint location methods, this algorithm has the advantages of significant denoising effectiveness and high localization accuracy.

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SDR based indoor localization using ambient WiFi and GSM signals

Cited by 13 publications

References 10 publications

Internet of Things: A Scientometric Review

Internet of Things: A Scientometric Review

Improved Time of Arrival measurement model for non‐convex optimization

A novel indoor localization method using passive phase difference fingerprinting based on channel state information

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