A deep learning spatiotemporal prediction framework for mobile crowdsourced services

Ben Said, Ahmed, Erradi, Abdelkarim, Ghari Neiat, Azadeh and Bouguettaya, Athman 2019, A deep learning spatiotemporal prediction framework for mobile crowdsourced services, Mobile Networks and Applications, vol. 24, no. 3, pp. 1120-1133, doi: 10.1007/s11036-018-1105-0.

Attached Files
Name Description MIMEType Size Downloads

Title A deep learning spatiotemporal prediction framework for mobile crowdsourced services
Author(s) Ben Said, Ahmed
Erradi, Abdelkarim
Ghari Neiat, AzadehORCID iD for Ghari Neiat, Azadeh orcid.org/0000-0001-7512-7143
Bouguettaya, Athman
Journal name Mobile Networks and Applications
Volume number 24
Issue number 3
Start page 1120
End page 1133
Total pages 15
Publisher Springer New York
Place of publication New York, N.Y.
Publication date 2019-06
ISSN 1383-469X
1572-8153
Summary 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.
Language eng
DOI 10.1007/s11036-018-1105-0
Indigenous content off
Field of Research 0805 Distributed Computing
0999 Other Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2018, Springer Science+Business Media
Persistent URL http://hdl.handle.net/10536/DRO/DU:30145179

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 24 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Tue, 10 Nov 2020, 09:53:46 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.