Crescendo: An Infrastructure-free Ubiquitous Cellular Network-based Localization System

Elbakly, Rizanne; Youssef, Moustafa

doi:10.1109/wcnc.2019.8885420

Cited by 16 publications

(4 citation statements)

References 26 publications

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“…Furthermore, this consumes virtually no extra power in addition to the normal phone operation. A number of cellular-based localization techniques have been proposed in literature for both indoor [23]- [26] and outdoor settings [27]- [32]. Nevertheless, current cellular-based positioning systems either try to learn the pattern of the received signal strength using probabilistic techniques, e.g.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning

Rizk

Shokry

Youssef

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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Recently, deep learning-based positioning systems have gained attention due to their higher performance relative to traditional methods. However, obtaining the expected performance of deep learning-based systems requires large amounts of data to train model. Obtaining this data is usually a tedious process which hinders the utilization of such deep learning approaches.In this paper, we introduce a number of techniques for addressing the data collection problem for deep learning-based cellular localization systems. The basic idea is to generate synthetic data that reflects the typical pattern of the wireless data as observed from a small collected dataset.Evaluation of the proposed data augmentation techniques using different Android phones in a cellular localization case study shows that we can enhance the performance of the localization systems in both indoor and outdoor scenarios by 157% and 50.5%, respectively. This highlights the promise of the proposed techniques for enabling deep learning-based localization systems.

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Section: Introductionmentioning

confidence: 99%

Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning

Rizk

Shokry

Youssef

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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“…The resume suggests that better results are likely achieved when there are more towers involved within the region having a lot of open space. For comparison, here we give the test results from other research in their testbed: i) Relative received signal strength [6]: 29 m mean error (Cairo, Egypt); ii) CellSense [7]: 30 m median (Cairo, Egypt); iii) Bayesian [7]: 52.8 m mean error (Tokyo, Japan); and iv) Pearson coefficient correlation [8]: 80% of error is below 300 (unmentioned location).…”

Section: Discussionmentioning

confidence: 99%

A network-based mobile positioning system using an optimization model

Sabri,

Kosasih

2024

IJAAS

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The expansion of cellular network coverage facilitates the advancement of research on network-based positioning. We are interested in the signal fingerprinting method to predict the location of a mobile device. By this method, the device must be within the fingerprint coverage to have a successful location prediction. However, any disturbance in the signal propagation would decrease the prediction accuracy. We propose an optimization model based on generalized triangulation combined with a signal fingerprint which is treated more adaptively in responding to any signal disturbance. The triangulation method determines the most likely region where the device is located. The solution provides the estimated longitude and latitude of the device. An illustration of the implementation of the model is presented. The model is assessed using the Indosat cellular network in three distinct testbeds in Indonesia, which are: South Jakarta, a metropolitan area; South Tangerang, a buffer area adjacent to the metropolitan area; and Malang, a city surrounded by rural areas. The most favorable outcome yields an average prediction error of 39.6 m, a maximum error of 197.08 m, a minimum error of 0.05 m, and a standard deviation of error of 39.22 m.

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“…The data relating to each sample, such as latitude, longitude, and cell towers IDs, is mapped to one specific grid cell according to the sample's coordinates. For example, Cellsense [24] and Crescendo [35] systems use the Radio Signal Strength Index (RSSI) distribution of the mobile devices located within the grid to be the unique fingerprint for every grid.…”

Section: B Fingerprint-based Methodsmentioning

confidence: 99%

DeepFeat: Robust Large-Scale Multi-Features Outdoor Localization in LTE Networks Using Deep Learning

Tharwat²,

Magdy³

et al. 2022

IEEE Access

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Location-based services in different applications push the research toward outdoor localization for users' equipment in Long Term Evolution (LTE) networks. Telecom operators can introduce valuable services to users based on their location, both in emergency and ordinary situations. This paper introduces DeepFeat: A deep-learning-based framework for outdoor localization using a rich feature set in LTE networks. DeepFeat works on the mobile operator side, and it leverages many mobile network features and other metrics to achieve high localization accuracy. In order to reduce computation and complexity, we introduce a feature selection module to choose the most appropriate features as inputs to the deep learning model. This module reduces the computation and complexity by around 20.6%, with enhancement in system accuracy. The feature selection module uses correlation and Chi-squared algorithms to reduce the feature set to 12 inputs only regardless of the area size, compared to a large number of cell towers in similar systems; such input increases exponentially with increasing the test area. In order to enhance the accuracy of DeepFeat, a One-to-Many augmenter is introduced to extend the dataset and improve the system's overall performance. The results show the impact of the proper features selection adopted by DeepFeat on the system performance. DeepFeat achieved median localization accuracy of 13.179m in an outdoor environment in a mid-scale area of 6.27Km 2 . In a large-scale area of 45Km 2 , the median localization accuracy is 13.7m. DeepFeat was compared to other state-of-the-art deep-learningbased localization systems that leverage a small number of features. We show that using the DeepFeat carefully selected feature set enhances the localization accuracy compared to the state-of-the-art systems by at least 286%.

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Crescendo: An Infrastructure-free Ubiquitous Cellular Network-based Localization System

Cited by 16 publications

References 26 publications

Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning

Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning

A network-based mobile positioning system using an optimization model

DeepFeat: Robust Large-Scale Multi-Features Outdoor Localization in LTE Networks Using Deep Learning

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