Mobile Social Data Learning for User-Centric Location Prediction With Application in Mobile Edge Service Migration

Recently, location prediction has attracted considerable research effort because of the popularity of location-based services, such as mobile advertising and recommendations. With the unprecedented proliferation of mobile social networks, such as WeChat and Twitter, we are able to use location service to bridge the online and offline worlds, which is of great significance to many smart city applications. Different from existing studies, in this paper, we promote a user-centric location prediction approach by leveraging a user's local mobile social information without involving other users' location privacy. We propose a factor graph learning model that integrates not only user's social and network information but also the correlations between a user's locations into a unified framework. Furthermore, we use ReliefF algorithm to select user-specific significant features for location prediction and define the measure of location entropy to study the similarity between location, network status, and social behavior. To show the benefit of precise location prediction, we further apply it to personalized service migration in mobile edge computing (MEC) and accordingly propose prediction-based amortizing algorithm and lazy migration algorithm that can well balance the tradeoff between migration cost and nonmigration latency in a cost-efficient manner. We conduct extensive experiments using a real-world data trace, which shows that our model performs much better in location prediction compared with several classic methods and the MEC service quality can be significantly enhanced by leveraging the location prediction.