Context-aware services based on spatio-temporal zoning and crowdsourcing

• Main Authors: Ahmad, Akhlaq, ., Md. Abdur Rahman, Wahiddin, Mohamed Ridza, Rehman, Faizan Ur, Khelil, Abdelmajid, Lbath, Ahmed
• Format: Article
• Language: English; English; English
• Published: Taylor and Francis Ltd. 2018
• Subjects: T58.5 Information technology; T58.6 Management information systems
• Online Access: http://irep.iium.edu.my/64739/2/64739_Context-aware%20services%20based_SCOPUS.pdf; http://irep.iium.edu.my/64739/13/64739_Context-aware%20services%20based%20on%20spatio-temporal.pdf; http://irep.iium.edu.my/64739/14/64739_Context-aware%20services%20based%20on%20spatio-temporal_WOS.pdf; http://irep.iium.edu.my/64739/; https://www.tandfonline.com/doi/full/10.1080/0144929X.2018.1476586?scroll=top&needAccess=true
• Institution: Universiti Islam Antarabangsa Malaysia

Crowdsourcing offers great opportunities to recognise user context and prescribe relevant services for both offline and real-time activities. In this work, we present a zoning model that leverages spatio-temporal dimensions and then employs different contexts to recommend necessary customised services. The context model takes into consideration three context sets: fully restricted, fully unrestricted and semi-restricted with respect to both spatial and temporal dimensions. As a proof of concept, we apply this zoning model in a scenario where a very large crowd get together to perform spatio-temporal activities. The user context of the heterogeneous crowd is captured using the carried smartphones, i.e. via crowdsourcing. Depending on the context sets and zone, the system can recommend a set of services to each user. The system has been deployed since 2014 to support the spatio-temporal activities of a very large crowd. We present our implementation details and the user feedback, which is very encouraging.