Space and Time Constrained Data Offloading in Vehicular Networks

Abstract

Mobile data offloading is a feasible and cost-effective solution to ease the burden of cellular networks. In Internet of Vehicles, however, existing offloading techniques are hardly applicable to the ubiquitous location-dependent services, which impose strict spatiotemporal constraints on content delivery. Particularly, the spatiotemporal constraints cause a phenomenon where the delivery deadlines are different even for the vehicles who subscribe to the same content. To this end, we propose a space and time constrained data offloading scheme (STCDO). The scheme maintains a probability-based contact graph to represent the near-term transmission opportunities between vehicles. Furthermore, a dynamic structure called offloading tree is introduced to evaluate the influence of each vehicle on opportunistic dissemination. Finally, the scheme uses a greedy algorithm to effectively select appropriate vehicles as offloading seeds. We perform extensive experiments based on the real-world map-driven movement model in the ONE simulator. The experimental results show that the proposed scheme largely offloads the overloaded cellular networks while satisfying the spatiotemporal constraints.