Future Research Directions
Although this monograph presents some important research topics of modeling and optimization for MSNs, there are still several open problems including but not limited to the followings.
Big Data and MSNs
In MSNs, massive data are generated by users for social activities by using mobile devices. The average amount of mobile data traffic per month in 2015 was 4.2EB/mo . Among the above traffic, there is a very large proportion caused by video and audio. In 2019, mobile data traffic is predicted to be an almost sixfold increase which will be 24.3 EB/mo. Mobile social data have the properties including the large volume, wide variety, fast velocity, and economic value which are called “4Vs” .
In addition to the existing “4Vs”, mobile big data in MSNs has some unique and important features including aggregate features and individual features. These features can be captured by some analytical methods such as data mining and machine learning. For aggregate features, it can be exploited to improve the efficiency of MSNs. For example, the wireless resource can be allocated based on the demand of users in the communities that have the same interest. For individual features, it is useful for network operators to provide the improved and personalized services. For example, social relations and mobile patterns of mobile users can be used to design an optimal content routing.
Mobile big data applications are helpful to provide users with better social services. For example, based on the preferences of users in a community, the content recommendation system can be built. The content which users may have interest can be delivered based on a publish/subscribe mode. How to apply the big data applications into MSNs is still a challenge.