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

The wireless network community has become increasingly awa re 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 environm ent 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 us ing both node-oriented and network-wide analysis and experime ntation, we discover that the optimal routing structure remain s quite stable even though the properties of individual links and ro utes vary significantly as traffic pattern changes. In cases where the o ptimal routing structure does change, data-driven link estimatio n and routing is either guaranteed to converge to the optimal structur e or empirically shown to converge to a close-to-optimal structur e. These findings provide the foundation for addressing the BLS issue in the presence of traffic-induced dynamics and suggest approache s ot r than existing ones. These findings also demonstrate that it i s possible to maintain an optimal, stable routing structure despit the fact that the properties of individual links and paths vary in res ponse to network dynamics.