DeepLoc: Deep Neural Network-based Telco Localization
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
Series
International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, pp. 258-267
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.Description
Other note
Citation
Zhang, Y, Xiao, Y, Zhao, K & Rao, W 2019, DeepLoc: Deep Neural Network-based Telco Localization. in International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ACM, pp. 258-267, International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Houston, Texas, United States, 12/11/2019. < http://10.1145/3360774.3360779 >