Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

Exploiting Sociality for Collaborative Message Dissemination in VANETs

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  • @INPROCEEDINGS{10.1007/978-3-030-12981-1_26,
        author={Weiyi Huang and Peng Li and Tao Zhang and Yu Jin and Heng He and Lei Nie and Qin Liu},
        title={Exploiting Sociality for Collaborative Message Dissemination in VANETs},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={2},
        keywords={Message disseminations VANETs Community collaboration degree},
        doi={10.1007/978-3-030-12981-1_26}
    }
    
  • Weiyi Huang
    Peng Li
    Tao Zhang
    Yu Jin
    Heng He
    Lei Nie
    Qin Liu
    Year: 2019
    Exploiting Sociality for Collaborative Message Dissemination in VANETs
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-12981-1_26
Weiyi Huang, Peng Li,*, Tao Zhang1, Yu Jin, Heng He, Lei Nie, Qin Liu2
  • 1: New York Institute of Technology
  • 2: Wuhan University
*Contact email: lipeng@wust.edu.cn

Abstract

Message dissemination problem have attracted great attention in vehicular ad hoc networks (VANETs). One important task is to find a set of relay nodes to maximize the number of successful delivery messages. In this paper, we investigate the message dissemination problem and propose a new method that aims at selecting optimal nodes as the collaborative nodes to distribute message. Firstly, we analyze the real vehicle traces and find its sociality by extracting contacts and using community detecting approach. Secondly, we propose community collaboration degree to measure the collaborative possibility of message delivery in the whole community. Moreover, we use Markov chains to infer future community collaboration degree. Thirdly, we design a community collaboration (CC) algorithm for selecting the optimal collaborative nodes. We compare our algorithm with other methods. The simulation results show that our algorithm performance is better than other methods.