Opportunistic WiFi Offloading in a Vehicular Environment: An MDP Approach

Abstract

In a vehicular network environment, vehicles can download data through opportunistically-encountered Roadside Units (RSUs) with a lower cost, compared to that from Base Stations (BSs). However, the delay experienced by the vehicles might be undesirably prolonged if they only download data through RSUs. In this paper, we aim to minimize the average delay under the constraint of average cost by scheduling the download rates from RSUs and BSs. One challenge lying in the design of the downloading policy is the uncertainty of the download condition, i.e., whether at least an RSU is available or BS only, in the future slots. To overcome this challenge, we notice that vehicles from opposite directions can share their known download conditions to reduce this uncertainty. To make the most of this information, a Markov decision process (MDP) is used to model the system operations, based on which, average delay and cost can be analyzed to formulate the optimization problem. By solving this problem, the delay-minimal downloading policy can be obtained to achieve the optimal delay-cost tradeoff in the considered vehicular network. Finally, performance improvement with the help of information sharing among vehicles is validated by extensive simulations.