LASS: Local-Activity and Social-Similarity Based Data Forwarding in Mobile Social Networks

This paper aims to design an efficient data forwarding scheme based on local activity and social similarity (LASS) for mobile social networks (MSNs). Various definitions of social similarity have been proposed as the criterion for relay selection, which results in various forwarding schemes. The appropriateness and practicality of various definitions determine the performances of these forwarding schemes. A popular definition has recently been proven to be more efficient than other existing ones, i.e., the more common interests between two nodes, the larger social similarity between them. In this work, we show that schemes based on such definition ignore the fact that members within the same community, i.e., with the same interest, usually have different levels of local activity, which will result in a low efficiency of data delivery. To address this, in this paper, we design a new data forwarding scheme for MSNs based on community detection in dynamic weighted networks, called Local-Activity and Social-Similarity, taking into account the difference of members’ internal activity within each community, i.e., local activity . To the best of our knowledge, the proposed scheme is the first one that utilizes different levels of local activity within communities. Through extensive simulations, we demonstrate that LASS achieves better performance than state-of-the-art protocols.