Game Theory for Networks. 2nd International ICST Conference, GAMENETS 2011, Shanghai, China, April 16-18, 2011, Revised Selected Papers

Research Article

A Game Theoretic Approach for Multi-hop Power Line Communications

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  • @INPROCEEDINGS{10.1007/978-3-642-30373-9_38,
        author={Walid Saad and Zhu Han and Harold Poor},
        title={A Game Theoretic Approach for Multi-hop Power Line Communications},
        proceedings={Game Theory for Networks. 2nd International ICST Conference, GAMENETS 2011, Shanghai, China, April 16-18, 2011, Revised Selected Papers},
        proceedings_a={GAMENETS},
        year={2012},
        month={10},
        keywords={},
        doi={10.1007/978-3-642-30373-9_38}
    }
    
  • Walid Saad
    Zhu Han
    Harold Poor
    Year: 2012
    A Game Theoretic Approach for Multi-hop Power Line Communications
    GAMENETS
    Springer
    DOI: 10.1007/978-3-642-30373-9_38
Walid Saad1,*, Zhu Han2,*, Harold Poor1,*
  • 1: Princeton University
  • 2: University of Houston
*Contact email: saad@princeton.edu, zhan2@mail.uh.edu, poor@princeton.edu

Abstract

In this paper, a model for multi-hop power line communication is studied in which a number of smart sensors, e.g., smart meters, seek to minimize the delay experienced during the transmission of their data to a common control center through multi-hop power line communications. This problem is modeled as a network formation game and an algorithm is proposed for modeling the dynamics of network formation. The proposed algorithm is based on a myopic best response process in which each smart sensor can autonomously choose the path that connects it to the control center through other smart sensors. Using the proposed algorithm, the smart sensors can choose their transmission path while optimizing a cost that is a function of the overall achieved transmission delay. This transmission delay captures a tradeoff between the improved channel conditions yielded by multi-hop transmission and the increase in the number of hops. It is shown that, using this network formation process, the smart sensors can self-organize into a tree structure which constitutes a Nash network. Simulation results show that the proposed algorithm presents significant gains in terms of reducing the average achieved delay per smart sensor of at least 28.7% and 60.2%, relative to the star network and a nearest neighbor algorithm, respectively.