Abstract
WiFi wireless access has been the basic living need for smart phone users in the era of mobile multimedia. A large number of WiFi hotspots have also developed into an important infrastructure of multimedia accessing in smart city. Learning the dynamic features of free-environment WiFi connections is of great help to both the customization of WiFi connection service and the strategy of mobile multimedia. While mobility prediction attracts much interest in human behavior research which is more focused on fixed environments such as university, home and office, etc., this paper investigates more challenging public regions like shopping malls. A WiFi dwell time estimation method is proposed from a crowdsourcing view, to tackle the lack of contextual information for a single individual in such free environments. This is achieved by a context-embedded longitudinal factorization (CELoF) method based on multi-way tensor factorization and experiments on real dataset demonstrate the efficacy of the proposed solution.
This work was part-funded by 973 Program under Grant No. 2011CB302206, National Natural Science Foundation of China under Grant Nos. 61272231, 61472204, 61502264, Beijing Key Laboratory of Networked Multimedia.
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Yan, C., Wang, P., Pang, H., Sun, L., Yang, S. (2017). CELoF: WiFi Dwell Time Estimation in Free Environment. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_41
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