5th International ICST Conference on Communications and Networking in China

Research Article

Cross-layer optimization for wireless sensor network with multi-packet reception

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  • @INPROCEEDINGS{10.4108/chinacom.2010.41,
        author={Lei Shi and Jiang-Hong Han and Yi Shi and Zhen-Chun Wei},
        title={Cross-layer optimization for wireless sensor network with multi-packet reception},
        proceedings={5th International ICST Conference on Communications and Networking in China},
        publisher={IEEE},
        proceedings_a={CHINACOM},
        year={2011},
        month={1},
        keywords={Multi-packet reception successive interference cancellation wireless sensor network capacity cross-layer optimization},
        doi={10.4108/chinacom.2010.41}
    }
    
  • Lei Shi
    Jiang-Hong Han
    Yi Shi
    Zhen-Chun Wei
    Year: 2011
    Cross-layer optimization for wireless sensor network with multi-packet reception
    CHINACOM
    ICST
    DOI: 10.4108/chinacom.2010.41
Lei Shi1, Jiang-Hong Han1, Yi Shi2, Zhen-Chun Wei1
  • 1: School of Computer & Information, Hefei University of Technology, Hefei, Anhui 230009, China
  • 2: Dept. of ECE, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Abstract

In this paper, we consider how to exploit multi-packet reception (MPR) to increase the capacity for a wireless sensor network. Since MPR behavior at the physical layer affects link layer scheduling, it is necessary to follow a cross-layer approach to obtain an optimal solution. Due to the complexity of cross-layer optimization, although MPR has great potential to increase capacity, optimal solutions are yet to be developed. We build constraints for the signal-to-noise-ratio requirement under MPR at the physical layer such that we can check the feasibility for a set of concurrent transmissions. We further develop an upper bound for the number of concurrent transmissions, which enables us to identify all feasible sets of concurrent transmissions in polynomial time. Then a capacity problem can be formulated as a linear program (LP) but with a large number of variables. We propose a concept of maximum feasible set to decrease the size of LP. Finally, by comparing optimal solutions with and without MPR, we show that network capacity can be increased about 100% by using MPR.