Social-similarity-based Multicast Routing Algorithms in Impromptu Mobile Social Networks
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Mobile Social Networks (MSNs) where people contact each other through mobile devices have become increasingly popular. In this thesis, we study a special kind of MSNs formed impromptu (IMSNs) when people gather together at conferences, social events, etc. Multicast is an important routing service which supports the dissemination of messages to a group of users. Most of the existing related multicast algorithms are designed for general Delay Tolerant Networks (DTNs) where social factors are neglected. Recently, a social-profile-based multicast (SPM) routing protocol that utilizes the static social features in user profiles has been proposed. We believe that in a dynamic environment such as the IMSN, static social features may not reflect people’s dynamic behavior. Therefore, in this work, we propose the concept of dynamic social features and enhanced dynamic social features to capture people’s contact behavior. Based on them, we design a novel social-similarity-based multicast algorithm (Multi-Sosim) and its enhancement (E-Multi-Sosim). Simulation results using a real conference trace representing an IMSN show that the E-Multi-Sosim algorithm performs better than the Multi-Sosim algorithm, which outperforms its variations and the existing one using static social features.