Abstract
Mobile Social Networks (MSNs) is a kind of opportunistic networks, which is composed of a large number of mobile nodes with social characteristic. Up to now, the prevalent communitybased routing algorithms mostly select the most optimal social characteristic node to forward messages. But they almost don't consider the effect of community distribution on mobile nodes and the time-varying characteristic of network. These algorithms usually result in high consumption of network resources and low successful delivery ratio if they are used directly in mobile social networks. We build a time-varying community-based network model, and propose a community-aware message opportunistic transmission algorithm (CMOT) in this paper. For inter-community messages transmission, the CMOT chooses an optimal community path by comparing the community transmission probability. For intra-community in local community, messages are forwarded according to the encounter probability between nodes. The simulation results show that the CMOT improves the message successful delivery ratio and reduces network overhead obviously, compared with classical routing algorithms, such as PRoPHET, MaxProp, Spray and Wait, and CMTS.
Paper Type
Event
Community-based Message Opportunistic Transmission
Mobile Social Networks (MSNs) is a kind of opportunistic networks, which is composed of a large number of mobile nodes with social characteristic. Up to now, the prevalent communitybased routing algorithms mostly select the most optimal social characteristic node to forward messages. But they almost don't consider the effect of community distribution on mobile nodes and the time-varying characteristic of network. These algorithms usually result in high consumption of network resources and low successful delivery ratio if they are used directly in mobile social networks. We build a time-varying community-based network model, and propose a community-aware message opportunistic transmission algorithm (CMOT) in this paper. For inter-community messages transmission, the CMOT chooses an optimal community path by comparing the community transmission probability. For intra-community in local community, messages are forwarded according to the encounter probability between nodes. The simulation results show that the CMOT improves the message successful delivery ratio and reduces network overhead obviously, compared with classical routing algorithms, such as PRoPHET, MaxProp, Spray and Wait, and CMTS.
Recommended Citation
Zhang, S., Tan, P., Bao, X., Song, W.W., & Liu, X. (2015). Community-based Message Opportunistic Transmission. In D. Vogel, X. Guo, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Transforming Healthcare through Information Systems (ISD2015 Proceedings). Hong Kong, SAR: Department of Information Systems. ISBN: 978-962-442-393-8. http://aisel.aisnet.org/isd2014/proceedings2015/Mobile/1.