4th International ICST Conference on Wireless Internet

Research Article

Towards Distributed Network Classification for Mobile Ad hoc Networks

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  • @INPROCEEDINGS{10.4108/ICST.WICON2008.4924,
        author={Dimitrios Antonellis and Ahmed Mansy and Konstantinos Psounis and Mostafa H. Ammar},
        title={Towards Distributed Network Classification for Mobile Ad hoc Networks},
        proceedings={4th International ICST Conference on Wireless Internet},
        publisher={ICST},
        proceedings_a={WICON},
        year={2010},
        month={5},
        keywords={classification mobile ad hoc networks intermittently connected networks routing},
        doi={10.4108/ICST.WICON2008.4924}
    }
    
  • Dimitrios Antonellis
    Ahmed Mansy
    Konstantinos Psounis
    Mostafa H. Ammar
    Year: 2010
    Towards Distributed Network Classification for Mobile Ad hoc Networks
    WICON
    ICST
    DOI: 10.4108/ICST.WICON2008.4924
Dimitrios Antonellis1,*, Ahmed Mansy2,*, Konstantinos Psounis1,*, Mostafa H. Ammar2,*
  • 1: Electrical Engineering Dept, University of Southern California
  • 2: College of Computing, Georgia Institute of Technology
*Contact email: dimitria@usc.edu, amansy@cc.gatech.edu, kpsounis@usc.edu, ammar@cc.gatech.edu

Abstract

Mobile ad hoc networks range from traditional MANETs where end-to-end paths exist from sources to destinations, to DTNs where no contemporaneous end-to-end paths exist and communication is achieved by the store, carry, and forward model of routing. Hence, nodes of these networks need to identify the level of connectivity of the network they belong to and classify it as a MANET or a DTN, in order to properly select appropriate protocols to achieve end-toend communication. What is more, since mobile ad hoc networks change over time and space, nodes need to periodically re-access their network classification to adapt to the always changing environment. Recently, there has been an effort to classify the various types of mobile ad hoc networks assuming there is a centralized authority that has complete knowledge of the network and its dynamics. In this paper we design distributed mechanisms for nodes to perform the above classification on the fly, based only on local information that they collect as they move and encounter other nodes. The mechanisms take advantage of a combination of measurements and analytical techniques. We investigate the accuracy of our mechanisms by comparing the network classification of the centralized authority to that of a node using our schemes.