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DeepLoc: deep neural network-based telco localization

Published: 03 February 2020 Publication History

Abstract

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.

References

[1]
Google maps for mobile. http://www.google.com/mobile/maps.
[2]
R. A. Becker, R. Cáceres, K. Hanson, S. Isaacman, J. M. Loh, M. Martonosi, J. Rowland, S. Urbanek, A. Varshavsky, and C. Volinsky. Human mobility characterization from cellular network data. Commun. ACM, 56(1):74--82, 2013.
[3]
R. A. Becker, R. Cáceres, K. Hanson, J. M. Loh, S. Urbanek, A. Varshavsky, and C. Volinsky. A tale of one city: Using cellular network data for urban planning. IEEE Pervasive Computing, 10(4):18--26, 2011.
[4]
C. Costa and D. Zeinalipour-Yazti. Telco big data: Current state & future directions. In IEEE MDM 2018, pages 11--14, 2018.
[5]
Y. Huang, W. Rao, F. Zhu, N. Liu, M. Yuan, J. Zeng, and H. Yang. Experimental study of telco localization methods. In IEEE MDM 2017, pages 299--306, 2017.
[6]
M. Ibrahim and M. Youssef. A hidden markov model for localization using low-end GSM cell phones. In IEEE ICC 2011, pages 1--5. IEEE, 2011.
[7]
M. Ibrahim and M. Youssef. Cellsense: An accurate energy-efficient GSM positioning system. IEEE Trans. Vehicular Technology, 61(1):286--296, 2012.
[8]
T. Kieu, B. Yang, and C. S. Jensen. Outlier detection for multidimensional time series using deep neural networks. In IEEE MDM 2018, pages 125--134. IEEE, 2018.
[9]
lames J. Caffery and G. L. Stuber. Overview of radiolocation in cdma cellular systems. IEEE Communications Magazine, 36(4):38--45, Apr. 1998.
[10]
I. Leontiadis, A. Lima, H. Kwak, R. Stanojevic, D. Wetherall, and K. Papagiannaki. From cells to streets: Estimating mobile paths with cellular-side data. In A. Seneviratne, C. Diot, J. Kurose, A. Chaintreau, and L. Rizzo, editors, ACM CoNEXT 2014, pages 121--132. ACM, 2014.
[11]
R. Margolies, R. A. Becker, S. D. Byers, S. Deb, R. Jana, S. Urbanek, and C. Volinsky. Can you find me now? evaluation of network-based localization in a 4g LTE network. In IEEE INFOCOM 2017, pages 1--9, 2017.
[12]
N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses, and N. S. Correal. Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4):54--69, 2005.
[13]
A. Ray, S. Deb, and P. Monogioudis. Localization of LTE measurement records with missing information. In IEEE INFOCOM 2016, pages 1--9, 2016.
[14]
X. Shi, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. Woo. Convolutional LSTM network: A machine learning approach for precipitation now casting. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, NIPS 2015, pages 802--810, 2015.
[15]
I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, editors, NIPS 2014, pages 3104--3112, 2014.
[16]
S. Swales, J. Maloney, and J. Stevenson. Locating mobile phones and the us wireless e-911 mandate. In Novel Methods of Location and Tracking of Cellular Mobiles and Their System Applications (Ref. No. 1999/046), IEE Colloquium on, pages 2--1. IET, 1999.
[17]
R. M. Vaghef, M. R. Gholami, and E. G. Ström. Rss-based sensor localization with unknown transmit power. In IEEE ICASSP 2011, pages 2480--2483. IEEE, 2011.
[18]
H. Wu, Z. Chen, W. Sun, B. Zheng, and W. Wang. Modeling trajectories with recurrent neural networks. In IJCAI 2017, pages 3083--3090, 2017.
[19]
H. Wu, W. Sun, B. Zheng, L. Yang, and W. Zhou. CLSTERS: A general system for reducing errors of trajectories under challenging localization situations. IMWUT, 1(3):115:1--115:28, 2017.
[20]
S. Yao, S. Hu, Y. Zhao, A. Zhang, and T. F. Abdelzaher. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In R. Barrett, R. Cummings, E. Agichtein, and E. Gabrilovich, editors, WWW 2017, pages 351--360. ACM, 2017.
[21]
Z. Yuan, X. Zhou, and T. Yang. Hetero-convlstm: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data. In Y. Guo and F. Farooq, editors, ACM SIGKDD 2018, pages 984--992. ACM, 2018.
[22]
C. Zhang, P. Patras, and H. Haddadi. Deep learning in mobile and wireless networking: A survey. CoRR, abs/1803.04311, 2018.
[23]
Y. Zhang, W. Rao, and Y. Xiao. Deep neural network-based telco outdoor localization. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, SenSys 2018, Shenzhen, China, November 4--7, 2018, pages 307--308, 2018.
[24]
K. Zhao, M. Musolesi, P. Hui, W. Rao, and S. Tarkoma. Explaining the power-law distribution of human mobility through transportation modality decomposition. Scientific reports, 5:9136, 2015.
[25]
P. Zhao, C. Zhang, Y. Huang, W. Rao, F. Zhu, N. Liu, J. Geng, M. Yuan, and J. Zeng. Demo abstract: Telco localization techniques. In 2017 IEEE Conference on Computer Communications Workshops, INFOCOM Workshops, Atlanta, GA, USA, May 1--4, 2017, pages 970--971, 2017.
[26]
Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: Concepts, methodologies, and applications. ACM TIST, 5(3):38:1--38:55, 2014.
[27]
F. Zhu, C. Luo, M. Yuan, Y. Zhu, Z. Zhang, T. Gu, K. Deng, W. Rao, and J. Zeng. City-scale localization with telco big data. In ACM CIKM 2016, pages 439--448, 2016.

Cited By

View all
  • (2024)Adversarial Machine Learning for Wireless LocalizationNetwork Security Empowered by Artificial Intelligence10.1007/978-3-031-53510-9_8(213-236)Online publication date: 24-Feb-2024
  • (2023)Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an OutlookIEEE Open Journal of the Communications Society10.1109/OJCOMS.2023.33249524(2733-2801)Online publication date: 2023
  • (2023)Novel LSTM-Based Approaches for Enhancing Outdoor Localization Accuracy in 4G NetworksIEEE Access10.1109/ACCESS.2023.334104711(140103-140115)Online publication date: 2023

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cover image ACM Other conferences
MobiQuitous '19: Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
November 2019
545 pages
ISBN:9781450372831
DOI:10.1145/3360774
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 February 2020

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Author Tags

  1. CNN
  2. LSTM
  3. deep neural network
  4. outdoor localization

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  • Research-article

Funding Sources

  • Academy of Finland
  • National Natural Science Foundation of China
  • The Fundamental Research Funds for the Central Universities

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MobiQuitous
MobiQuitous: Computing, Networking and Services
November 12 - 14, 2019
Texas, Houston, USA

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Overall Acceptance Rate 26 of 87 submissions, 30%

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Cited By

View all
  • (2024)Adversarial Machine Learning for Wireless LocalizationNetwork Security Empowered by Artificial Intelligence10.1007/978-3-031-53510-9_8(213-236)Online publication date: 24-Feb-2024
  • (2023)Localization as a Key Enabler of 6G Wireless Systems: A Comprehensive Survey and an OutlookIEEE Open Journal of the Communications Society10.1109/OJCOMS.2023.33249524(2733-2801)Online publication date: 2023
  • (2023)Novel LSTM-Based Approaches for Enhancing Outdoor Localization Accuracy in 4G NetworksIEEE Access10.1109/ACCESS.2023.334104711(140103-140115)Online publication date: 2023

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