A Deep Learning Spatiotemporal Prediction Framework for Mobile Crowdsourced Services
Author | Ben Said, Ahmed |
Author | Erradi, Abdelkarim |
Author | Neiat, Azadeh Ghari |
Author | Bouguettaya, Athman |
Available date | 2020-05-14T09:55:45Z |
Publication Date | 2019 |
Publication Name | Mobile Networks and Applications |
Resource | Scopus |
ISSN | 1383-469X |
Abstract | This papers presents a deep learning-based framework to predict crowdsourced service availability spatially and temporally. A novel two-stage prediction model is introduced based on historical spatio-temporal traces of mobile crowdsourced services. The prediction model first clusters mobile crowdsourced services into regions. The availability prediction of a mobile crowdsourced service at a certain location and time is then formulated as a classification problem. To determine the availability duration of predicted mobile crowdsourced services, we formulate a forecasting task of time series using the Gramian Angular Field. We validated the effectiveness of the proposed framework through multiple experiments. - 2018, Springer Science+Business Media, LLC, part of Springer Nature. |
Sponsor | grant # NPRP9-224-1-049 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Springer New York LLC |
Subject | Classification Crowdsourced service Crowdsourced service availability prediction Deep learning Gramian angular field Spatio-temporal |
Type | Article |
Pagination | 1120-1133 |
Issue Number | 3 |
Volume Number | 24 |
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