4th International ICST Conference on Wireless Internet

Research Article

On biased link sampling in data-driven link estimation and routing in low-power wireless networks

Download67 downloads
  • @INPROCEEDINGS{10.4108/ICST.WICON2008.4882,
        author={Hongwei Zhang and Lifeng Sang and Anish Arora},
        title={On biased link sampling in data-driven link estimation and routing in low-power wireless networks},
        proceedings={4th International ICST Conference on Wireless Internet},
        publisher={ICST},
        proceedings_a={WICON},
        year={2010},
        month={5},
        keywords={Low-power wireless networks sensor networks data-driven link estimation and routing biased link sampling convergence stability},
        doi={10.4108/ICST.WICON2008.4882}
    }
    
  • Hongwei Zhang
    Lifeng Sang
    Anish Arora
    Year: 2010
    On biased link sampling in data-driven link estimation and routing in low-power wireless networks
    WICON
    ICST
    DOI: 10.4108/ICST.WICON2008.4882
Hongwei Zhang1,*, Lifeng Sang2,*, Anish Arora2,*
  • 1: Department of Computer Science, Wayne State University, USA.
  • 2: Department of Computer Sci. & Eng., The Ohio State University, USA
*Contact email: hzhang@cs.wayne.edu, sangl@cse.ohio-state.edu, anish@cse.ohio-state.edu

Abstract

The wireless network community has become increasingly aware of the benefits of data-driven link estimation and routing as compared with beacon-based approaches, but the issue of biased link sampling (BLS) has not been well studied even though it affects routing convergence in the presence of network and environment dynamics. Focusing on traffic-induced dynamics, we examine the open, unexplored question of how serious the BLS issue is and how to effectively address it when the routing metric ETX is used. For a wide range of traffic patterns and network topologies and using both node-oriented and network-wide analysis and experimentation, we discover that the optimal routing structure remains quite stable even though the properties of individual links and routes vary significantly as traffic pattern changes. In cases where the optimal routing structure does change, data-driven link estimation and routing is either guaranteed to converge to the optimal structure or empirically shown to converge to a close-to-optimal structure. These findings provide the foundation for addressing the BLS issue in the presence of traffic-induced dynamics and suggest approaches other than existing ones. These findings also demonstrate that it is possible to maintain an optimal, stable routing structure despite the fact that the properties of individual links and paths vary in response to network dynamics.