research-article

MonoDCell: A Ubiquitous and Low-Overhead Deep Learning-based Indoor Localization with Limited Cellular Information

Authors:

Moustafa YoussefAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 109 - 118

https://doi.org/10.1145/3347146.3359065

Published: 05 November 2019 Publication History

Abstract

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Despite the pervasive nature of cellular-based solutions, their localization quality depends on the number of cell towers provided by the phone, which is typically limited. Specifically, according to the standard, any cell phone can receive signal strength information from up to seven cell towers. However, the majority of cell phones usually return only the associated cell tower information, significantly limiting the amount of information available to the location determination algorithm, degrading its performance.

In this paper, we present MonoDCell: a novel cellular-based indoor localization system based on a deep long short-term memory (LSTM) network. The system utilizes the signal strength history from only the associated cell tower to achieve a fine-grained localization. MonoDCell incorporates different modules that lessen the data collection effort and improve the deep model's generalization and robustness against noise.

We deployed MonoDCell using different Android devices in two realistic testbeds of different sizes. Evaluation results show that it can track the user with median location error of 0.95m and 1.42m in the smaller and the larger testbeds, respectively. This accuracy demonstrates the superiority of MonoDCell compared to the state-of-the-art systems by at least 202% in both testbeds considered. This highlights the promise of MonoDCell as an accurate and ubiquitous localization system.

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https://doi.org/10.1109/TVT.2024.3454208
Hany MRizk HYoussef M(2024)AirTags for Human Localization, Not Just ObjectsProceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3681768.3698497(13-18)Online publication date: 29-Oct-2024
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Mostafa SYoussef MHarras K(2024)ModeSense: Ubiquitous and Accurate Transportation Mode Detection using Serving Cell Tower InformationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691250(184-195)Online publication date: 29-Oct-2024
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Index Terms

MonoDCell: A Ubiquitous and Low-Overhead Deep Learning-based Indoor Localization with Limited Cellular Information
1. Human-centered computing
  1. Ubiquitous and mobile computing
2. Networks
  1. Network services
    1. Location based services

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 ACM.

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 the author(s) 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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Published: 05 November 2019

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November 5 - 8, 2019

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

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https://doi.org/10.1109/TVT.2024.3454208
Hany MRizk HYoussef M(2024)AirTags for Human Localization, Not Just ObjectsProceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies10.1145/3681768.3698497(13-18)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3681768.3698497
Mostafa SYoussef MHarras K(2024)ModeSense: Ubiquitous and Accurate Transportation Mode Detection using Serving Cell Tower InformationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691250(184-195)Online publication date: 29-Oct-2024
https://dl.acm.org/doi/10.1145/3678717.3691250
Shen ZYang KZhao XZou JDu WWu J(2024)DMM: A Deep Reinforcement Learning Based Map Matching Framework for Cellular DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338388136:10(5120-5137)Online publication date: Oct-2024
https://doi.org/10.1109/TKDE.2024.3383881
Mostafa SYoussef MHarras K(2024)Accurate and Ubiquitous Floor Identification at the Edge using a Single Cell Tower2024 IEEE/ACM Symposium on Edge Computing (SEC)10.1109/SEC62691.2024.00024(206-219)Online publication date: 4-Dec-2024
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Rizk HUchiyama AYamaguch H(2024)Adaptability Matters: Heterogeneous Graphs for Agile Indoor Positioning in Cluttered Environments2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00033(103-108)Online publication date: 24-Jun-2024
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Mohsen MRizk HYamaguchi HYoussef M(2023)LocFreeProceedings of the 2nd ACM SIGSPATIAL International Workshop on Spatial Big Data and AI for Industrial Applications10.1145/3615888.3627813(32-40)Online publication date: 13-Nov-2023
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Mostafa SHarras KYoussef M(2023)Ubiquitous Transportation Mode Estimation using Limited Cell Tower Information2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)10.1109/VTC2023-Spring57618.2023.10200431(1-5)Online publication date: Jun-2023
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Trevlakis SBoulogeorgos APliatsios DQuerol JNtontin KSarigiannidis PChatzinotas SDi Renzo M(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
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