Amortized Fairness for Drive-Thru Internet

Abstract

The drive-thru Internet is an effective mean to provide Internet access service for wireless sensor networks deployed on vehicles. In these networks, vehicles often experience different link qualities due to different relative positions to the access point. This makes fair and efficient system design a very challenging task. In traditional approaches, the network efficiency has to be greatly sacrificed to provide the fair share for vehicles with low link quality. To address this issue, we propose a novel amortized fairness MAC protocol. The basic idea is that vehicles with lower link quality can defer their fairness requests and let the lost fairness be “amortized” in the future when their links become the high quality. The amortized fairness MAC requires predictions of future link quality. For this, we fully exploit the inner and inter-AP correlations revealed from our extensive field studies and design a link quality prediction algorithm. Based on the predicted link quality, we formulate the optimal amortized fairness MAC as a convex programming problem, which can be solved with the desired precision in polynomial time. Extensive simulation on real traces shows that the amortized fairness MAC scheme is more efficient than the existing fairness schemes in terms of efficiency and fairness.