Estimating locations and coverage areas of mobile network cells based on crowdsourced data

Neidhardt, Eric; Uzun, Abdulbaki; Bareth, Ulrich; Küpper, Axel

doi:10.1109/wmnc.2013.6549010

Cited by 25 publications

(40 citation statements)

References 10 publications

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“…The data is acquired by a crowdsourcing community using the OpenMobileNetwork for Android (OMNApp) 6 and Jewel Chaser 7 apps [25]. These applications collect network measurements on a user's mobile device, which are then sent to the OpenMobileNetwork server in order to compute cell locations as well as cell coverage information [26]. The estimated topology data is semantically modeled using RDF 8 , RDF Schema and other vocabularies enabling an easy integration of the dataset into the Linking Open Data Cloud 9 by defined links to related datasets, such as LinkedGeoData 10 , Linked Food 11 , or DBpedia 12 .…”

Section: A Data Preprocessingmentioning

confidence: 99%

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Göndör

Uzun

Rohrmann³

et al. 2013

2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications

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With approx. 6 million macro cells worldwide and a gross energy consumption of approx. 100 TWh per year as of 2013, mobile networks are one of the major energy consumers in the ICT sector. As trends, such as cloud-based services and other traffic-intensive mobile applications, fuel the growth of mobile traffic demands, operators of mobile telephony networks are forced to continuously extend the capacity of the existing infrastructure by both implementing new technologies as well as by installing new cell towers to provide more bandwidth for mobile users and improve the network's coverage. In order to implement energy-efficient reconfiguration mechanisms in mobile telephony networks as proposed by the project Communicate Green, it is essential to anticipate traffic hotspots, so that a network's configuration can be adjusted in time accordingly. Hence, predicting the movement of mobile users on a cellular level of the mobile network is a crucial task. In this paper, we propose a Movement Prediction System based on the algorithm of Yavas et al. that allows to determine the future movement of a user on a cellular level using precomputed movement patterns. We extended the algorithm to be capable of preselecting patterns based on time and contextual data in order to improve prediction accuracy. The performance of the algorithm is evaluated based on real and live user movement data from the OpenMobileNetwork, which is a platform providing estimated mobile network topology data. We found that the algorithm's prediction quality is sufficient, but requires an extensive amount of recorded user movements to perform well.

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Section: A Data Preprocessingmentioning

confidence: 99%

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Göndör

Uzun

Rohrmann³

et al. 2013

2013 International Conference on MOBILe Wireless MiddleWARE, Operating Systems, and Applications

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“…This is done using an application specially designed to improve the AL decision. Neidhardt et al [12] reports that using many of the existing open datasets leads to low accuracy because of extreme outliers and few measurements for some of the cells. They note that cellular location providers do not provide their complete data.…”

Section: Introductionmentioning

confidence: 99%

Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users

et al. 2016

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Abstract-The increasing demand for video streaming services with high Quality of Experience (QoE) has prompted a lot of research on client-side adaptation logic approaches. However, most algorithms use the client's previous download experience and do not use a crowd knowledge database generated by users of a professional service. We propose a new crowd algorithm that maximizes the QoE. Additionally, we show how crowd information can be integrated into existing algorithms and illustrate this with two state-of-the-art algorithms. We evaluate our algorithm and state-of-the-art algorithms (including our modified algorithms) on a large, real-life crowdsourcing dataset that contains 336,551 samples on network performance. The dataset was provided by WeFi LTD. Our new algorithm outperforms all other methods in terms of QoS (eMOS).

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“…However, the system does not analysis any cell data shape. Eric et al [3] proposed an approach to estimate position of base station out of crowd-sourced data using open mobile network. Semantic principles of linked data are used to publish mobile network.…”

Section: Introductionmentioning

confidence: 99%

“…However, our proposed system improves cell data shape for better user location purposes. Neidhardt et al [15] described a grid-based approach for estimating better accuracy of base station positions compared with their real locations. An OpenMobileNetwork has been used to visualize the base station positions on the OpenMobileNetwork Map.…”

Section: Introductionmentioning

confidence: 99%

Visualization and cell data analysis tool based on XML log files

Talukder

Andersson

Ahsan

2015

2015 IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops)

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Abstract-Mobility management is an essential feature of cellular networks. High accuracy of mobile user positioning is needed to handle mobility efficiently enough and bad cell data can harm this feature significantly. Inaccuracy of cell shapes, lack of cell data measurements, and inaccurate coordination in a geographical area are major shortcomings when it comes to positioning of mobile users in cellular networks. This paper describes a tool that visualizes and analyzes cell data based on XML log files. The tool evaluates a mathematical expression to identify bad cells from the log file and successfully fixes most of the bad cells identified. The tool repairs bad cell shapes in order to achieve better positioning of mobile users.

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Estimating locations and coverage areas of mobile network cells based on crowdsourced data

Cited by 25 publications

References 10 publications

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Predicting User Mobility in Mobile Radio Networks to Proactively Anticipate Traffic Hotspots

Adaptation logic for HTTP dynamic adaptive streaming using geo-predictive crowdsourcing for mobile users

Visualization and cell data analysis tool based on XML log files

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