دانلود رایگان مقاله الگوریتم در شبکه فرصت طلب اجتماعی موبایل با استفاده از ارتباطات و ویژگی اجتماعی

Abstract

Opportunistic Mobile Social Networks (OMSNs), formed by people moving around carrying mobile devices, enhance spontaneous communication among users that opportunistically encounter each other without additional infrastructure. Multicast is an important communication service in OMSNs. Most of the existing multicast algorithms neglect or adopt static social factors that are inadequate to catch nodes’ dynamic contact behavior. In this paper, we introduce dynamic social features and its enhancement to capture nodes’ contact behavior, consider more social relationships among nodes, and adopt community structure in the multicast compare-split schemes to select the best relay nodes to improve multicast efficiency. We propose two multicast algorithms based on these new features. The first one Multi-CSDO involves destination nodes only in community detection while the second one Multi-CSDR involves both the destination nodes and the relay candidates in community detection. The analysis of the algorithms is given and simulation results using two real OMSN traces show that our new algorithms outperform the existing ones in delivery rate, latency, and number of forwardings.

7. Conclusion

In this paper, we proposed two novel community and social feature-based multicast algoirthms Multi-CSDR and Multi-CSDO for OMSNs. In the algorithms, we used enhanced dynamic social features to more accurately capture nodes’ contact behavior, considered more social relationships among nodes, and proposed compare-split schemes based on community detection to select the best relay node for each destination in each hop to improve multicast efficiency. Analysis of the algorithms was given and simulation results using two real traces of OMSNs showed that our new algorithms consistently outperform the existing ones in delivery rate, latency, and number of forwardings. Right now, the community detection in the proposed algorithms uses the social features in user profiles. Relative to the online social features such as friendship, we refer to them as offline social features. As observed by [31], the Facebook friendship (online social features) graph is always more similar to the Bluetooth contacts graph than the interests (offline social features) graph. In the future, we will explore the possibility of improving the multicast algorithms using online social features such as friendship and the combination of both online and offline social features and test them theoretically and experimentally.