Learn on the Fly: Data-Driven Link Estimation and Routing in Sensor Network Backbones
Hui Zhang, Anish Arora, Pratik Kumar Sinha · 2006
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 estimates link quality based on data traffic, and it chooses routes by way of a locally measurable metric ELD, the expected MAC latency per unit-distance to the destination. 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 existing protocols, represented by the geography-unaware ETX and the geography-based PRD. We find that LOF reduces endto-end MAC latency by a factor of 3, enhances energy efficiency by a factor up to 2.37, improves route stability by 2 orders of magnitude, and improves network throughput by a factor up to 7.78. The results demonstrate the feasibility as well as potential benefits of data-driven link estimation and routing. Keywords—sensor network, beacon-free geographic routing, data-driven link quality estimation, MAC latency, IEEE 802.11b, real time, energy, reliability