Sustainable Data Usage for Predicting Consumer Behaviour
A special issue of Sustainability (ISSN 2071-1050).
Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 312
Special Issue Editor
Interests: data mining; natural language processing; machine learning; traffic; mobility
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Mobility makes up an important part of consumer behavior, as many services are still physical, and mobility is necessary to reach them. Mobility data generated particularly via mobile phones are enormous, and there are potentially many applications at the same time generating such potentially redundant data and storing them in the cloud. While data transfer consumes energy, data also need to process, and many applications utilizing techniques such as machine learning are also computationally intensive.
Sustainability in terms of data usage may be improved in a number of ways, such as adapting the data collection rate based on the application, edge computing, or simply using computationally more efficient analysis methods or green computing. While these aspects have started to attract more attention recently, there is still a lot of room for improvement.
You may find general information on mobility data in [1], while References [2–5] give examples of different approaches for dealing with mobility data.
[1] N. Pelekis, Y. Theodoridis, Mobility Data Management and Exploration, Springer.
[2] M. Bagheri, M. Siekkinen, JK Nurminen, Cellular-based vehicle to pedestrian (V2P) adaptive communication for collision avoidance, Proc. 2014 ICCVE, 450-456.
[3] Zi Wang; Zhiwei Zhao; Geyong Min; Xinyuan Huang; Qiang Ni; Rong Wang, User mobility aware task assignment for Mobile Edge Computing, Future Generation Computer Systems, ISSN: 0167-739X, Vol: 85, Page: 1-8
[4] W. Hou, Z. Ning and L. Guo, "Green Survivable Collaborative Edge Computing in Smart Cities," in IEEE Transactions on Industrial Informatics, vol. 14, no. 4, pp. 1594-1605, April 2018, doi: 10.1109/TII.2018.2797922.
[5] Qi, Bozhao, Lei Kang, and Suman Banerjee. "A vehicle-based edge computing platform for transit and human mobility analytics." Proceedings of the Second ACM/IEEE Symposium on Edge Computing. 2017.
Prof. Jyrki Nummenmaa
Guest Editor
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Keywords
- mobility data
- transportation
- traffic
- machine learning
- sustainability
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