Multiple content dissemination in roadside-unit-aided vehicular opportunistic networks

Roadside units (RSUs), which enable vehicle-to-infrastructure communications, are deployed along roadsides to handle the growing communication demands as the number of vehicles increases. The current opportunistic RSU-aided content dissemination schemes, however, do not address heterogeneous networks in terms of data items and users. We establish a mathematical framework to study the problem of multiple content dissemination under realistic RSU-aided opportunistic network assumptions, where: 1) mobile content items are heterogeneous in terms of size and lifetime; 2) vehicles' interests are different to different data; and 3) the RSU' storage for content dissemination is limited in size. We formulate the maximum data dissemination as a submodular function maximization (SFM) problem with multiple linear constraints (MLCs) of limited storage. Then, we propose an efficient heuristic algorithm to solve this NP-hard problem. Finally, we demonstrate the effectiveness of our algorithm through extensive simulations using realistic vehicular traces. The simulation results show that our proposed low-complexity heuristic algorithm performs much better than the existing feasible solutions, and it achieves similar performance to that of the most accurate algorithm currently available, whose computational complexity is unacceptable in practice.