Locating mobile phones and the US wireless E-911 mandate

Swales, SC; Maloney, Jason; Stevenson, Joseph

doi:10.1049/ic:19990239

Cited by 16 publications

(7 citation statements)

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“…Telco Location Recovery: Existing works on Telco position recovery are typically divided into three categories. Firstly, measurement-based methods approaches localize mobile devices based on absolute point-to-point distance or angles [20], [30], [32]. Triangulation localization, one of the widely used measurement-based approach, usually does not work well for 4G LTE MR data where frequently signal strengths of one or at most two cells are available.…”

Section: Preliminariesmentioning

confidence: 99%

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

Zhang,

Rao,

Zhang

et al. 2021

Preprint

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Recent years have witnessed unprecedented amounts of data generated by telecommunication (Telco) cellular networks. For example, measurement records (MRs) are generated to report the connection states between mobile devices and Telco networks, e.g., received signal strength. MR data have been widely used to localize outdoor mobile devices for human mobility analysis, urban planning, and traffic forecasting. Existing works using first-order sequence models such as the Hidden Markov Model (HMM) attempt to capture spatio-temporal locality in underlying mobility patterns for lower localization errors. The HMM approaches typically assume stable mobility patterns of the underlying mobile devices. Yet real MR datasets exhibit heterogeneous mobility patterns due to mixed transportation modes of the underlying mobile devices and uneven distribution of the positions associated with MR samples. Thus, the existing solutions cannot handle these heterogeneous mobility patterns. we propose a multi-task learning-based deep neural network (DNN) framework, namely PRNet + , to incorporate outdoor position recovery and transportation mode detection. To make sure the framework work, PRNet + develops a feature extraction module to precisely learn local-, short-and long-term spatio-temporal locality from heterogeneous MR samples. Extensive evaluation on eight datasets collected at three representative areas in Shanghai indicates that PRNet + greatly outperforms state-of-the-arts.

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Section: Preliminariesmentioning

confidence: 99%

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

Zhang,

Rao,

Zhang

et al. 2021

Preprint

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“…The literature works on Telco localization are typically divided into three categories. First, measurement-based methods localize mobile devices by estimating absolute point-to-point distance or angles [12,16,17]. However, triangulation-based approaches which require signal strength measurements of at least three cells do not work well in 4G LTE networks, since 4G LTE MRs often contain measurements of at most two cells.…”

Section: Telco Localizationmentioning

confidence: 99%

DeepLoc

Zhang

Xiao

Zhao

et al. 2019

Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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Recent years have witnessed unprecedented amounts of telecommunication (Telco) data generated by Telco radio and core equipment. For example, measurement records (MRs) are generated to report the connection states, e.g., received signal strength at the mobile device, when mobile devices give phone calls or access data services. Telco historical data (e.g., MRs) have been widely analyzed to understand human mobility and optimize the applications such as urban planning and traffic forecasting. The key of these applications is to precisely localize outdoor mobile devices from these historical MR data. Previous works calculate the location of a mobile device based on each single MR sample, ignoring the sequential and temporal locality hidden in the consecutive MR samples. To address the issue, we propose a deep neural network (DNN)-based localization framework namely DeepLoc to ensemble a recently popular sequence learning model LSTM and a CNN. Without skillful feature design and post-processing steps, DeepLoc can generate a smooth trajectory consisting of accurately predicted locations. Extensive evaluation on 6 datasets collected at three representative areas (core business, urban and suburban areas in Shanghai, China) indicates that DeepLoc greatly outperforms 10 counterparts.

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“…With an advancement of computing devices and radio frequency (RF) technologies, the need for locationbased services has begun to extend beyond military use. Today, wireless localization can be found in a wide variety of industries, including security, commercial, healthcare, and tourism, with applications ranging from intruder detection [1,2], asset tracking [3][4][5], and emergency call locating system [6][7][8] to capsule endoscope tracking [9][10][11], geo-fencing [12,13], and automated museum tour [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An ensemble approach to deep‐learning‐based wireless indoor localization

Wisanmongkol

Taparugssanagorn

Tran

et al. 2022

IET Wireless Sensor Systems

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The authors investigate the use of deep learning in wireless indoor localization to address the shortcomings of the existing range-based (e.g. trilateration and triangulation) and range-free (e.g. fingerprinting) localization. Instead of relying on geometric models and hand-picked features, deep learning can automatically extract the relationship between the observed data and the target's location. Nevertheless, a deep neural network (DNN) model providing a satisfactory accuracy might perform differently when it is retrained in the deployment. To mitigate this issue, the authors propose an ensemble method where DNN models obtained from multiple training sessions are combined to locate the target. In the authors' evaluation, several DNN models are trained on the data, which consists of the received signal strength (RSS), angle of arrival (AOA), and channel state information (CSI), used in the existing hybrid RSS/AOA and RSS/CSI fingerprinting, and their rootmean-square error (RMSE) values are compared accordingly. The results show that the proposed method achieves the lower RMSE than the existing methods, and the RMSE can be lowered by up to 1.47 m compared with the ones obtained from a single model. Moreover, for some DNN models, the RMSE values are even lower than the minimum RMSE obtained by their single-model counterparts.

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Locating mobile phones and the US wireless E-911 mandate

Cited by 16 publications

References 0 publications

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

DeepLoc

An ensemble approach to deep‐learning‐based wireless indoor localization

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