On the convergence and stability of data-driven link estimation and routing in sensor networks
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Comparison of data-driven link estimation methods in low-power wireless networks
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
LENS: resource specification for wireless sensor network experimentation infrastructures
WiNTECH '11 Proceedings of the 6th ACM international workshop on Wireless network testbeds, experimental evaluation and characterization
Experimental analysis of link estimation methods in low power wireless networks
WASA'11 Proceedings of the 6th international conference on Wireless algorithms, systems, and applications
Taming uncertainties in real-time routing for wireless networked sensing and control
Proceedings of the thirteenth ACM international symposium on Mobile Ad Hoc Networking and Computing
Adaptive instantiation of the protocol interference model in wireless networked sensing and control
ACM Transactions on Sensor Networks (TOSN)
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In the context of IEEE 802.11b network testbeds, we examine the differences between unicast and broadcast link properties, and we show the inherent difficulties in precisely estimating unicast link properties via those of broadcast beacons even if we make the length and transmission rate of beacons be the same as those of data packets. To circumvent the difficulties in link estimation, we propose to estimate unicast link properties directly via data traffic itself without using periodic beacons. To this end, we design a data-driven routing protocol Learn-on-the-Fly (LOF). LOF chooses routes based on ETX/ETT-type metrics, but the metrics are estimated via MAC feedback for unicast data transmission instead of broadcast beacons. Using a realistic sensor network traffic trace and an 802.11b testbed of ~195 Stargates, we experimentally compare the performance of LOF with that of beacon-based protocols, represented by the geography-unaware ETX and the geography-based PRD. We find that LOF reduces end-to-end MAC latency by a factor of 3, enhances energy efficiency by a factor up to 2.37, and improves network throughput by a factor up to 7.78, which demonstrate the feasibility and the potential benefits of data-driven link estimation and routing.