On the Convergence and Stability of Data-Driven Link Estimation and Routing in Sensor Networks

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Executive Summary

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, the authors 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, they discover that the optimal routing structure remains quite stable even though the properties of individual links and routes vary significantly as traffic pattern changes.

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