GPS: A method for data sharing in Mobile Social Networks

In Mobile Social Networks (MSNs), users with specific relationships are usually treated as a community for data sharing. However, the demand of data sharing among distributed strangers also exists. Those users that have the same interest but do not necessarily know or usually encounter each other can form a gossip community and share information. This paper proposes a data dissemination approach, i.e., the Gathering Point-aided Spreading (GPS) algorithm, which explores the encounter pattern of users and the aid of gathering points to facilitate data sharing in the gossip community. Based on the past encounter pattern, the GPS algorithm predicts the encounter probability among users and assigns the best users to carry the data for a wide spreading. Moreover, by storing a copy of data at the gathering points, GPS enables a further sharing of the data even the carriers leave the gathering points. With different utility functions, GPS can be modified into three versions (GPS-DR, GPS-DE and GPS-TR). Simulation experiments show that GPS outperforms SocialCast in both delivery ratio and delay in data sharing. In addition, among the three versions, GPS-DR and GPS-DE perform the best in terms of delivery ratio and delay respectively, while GPS-TR makes a tradeoff between them.