Ad Hoc Networks. 6th International ICST Conference, ADHOCNETS 2014, Rhodes, Greece, August 18-19, 2014, Revised Selected Papers

Research Article

A Social-Based Approach for Message Dissemination in Vehicular Ad Hoc Networks

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  • @INPROCEEDINGS{10.1007/978-3-319-13329-4_3,
        author={Alexandra Stagkopoulou and Pavlos Basaras and Dimitrios Katsaros},
        title={A Social-Based Approach for Message Dissemination in Vehicular Ad Hoc Networks},
        proceedings={Ad Hoc Networks. 6th International ICST Conference, ADHOCNETS 2014, Rhodes, Greece, August 18-19, 2014, Revised Selected Papers},
        proceedings_a={ADHOCNETS},
        year={2014},
        month={11},
        keywords={Multipoint relays Broadcasting protocols Influential spreaders Vehicular ad hoc networks OLSR},
        doi={10.1007/978-3-319-13329-4_3}
    }
    
  • Alexandra Stagkopoulou
    Pavlos Basaras
    Dimitrios Katsaros
    Year: 2014
    A Social-Based Approach for Message Dissemination in Vehicular Ad Hoc Networks
    ADHOCNETS
    Springer
    DOI: 10.1007/978-3-319-13329-4_3
Alexandra Stagkopoulou1,*, Pavlos Basaras1,*, Dimitrios Katsaros1,*
  • 1: University of Thessaly & CERTH
*Contact email: alstagop@inf.uth.gr, pabasara@inf.uth.gr, dkatsar@inf.uth.gr

Abstract

The spreading of messages in a vehicular network is an important task and finds many applications in Intelligent Transportation Systems (ITS). A common problem to this direction is to select an appropriate set of vehicles that on behalf of a sender will further rebroadcast the message and reduce redundant retransmission. Of particular interest is the use of social inspired metrics to identify potent vehicles which can set the right path for the spreading of messages and cover a wide range of a vehicular network. In this work we propose a novel approach for selecting vehicles based on the , which accounts for the number of directed and diverse paths emanating from each individual vehicle. We evaluated our approach and compared with the standard IETF, . Our experimental results show that outperforms its competitor in various network conditions by at least 10%.