| Summary: | The paper studies the application of various content distribution policies for vehicular ad hoc networks (VANETs) and compares their effectiveness under various simulation scenarios. Our implementation augments the existing Veins tool, an open source framework for vehicular network simulations based on the discrete event simulator OMNET++ and SUMO, a tool that simulates traffic on road networks. The proposed solution integrates various additional features into the pre-existing Veins realizations and expands them to include the modeling and implementation of proposed caching and content distribution policies and the measurement of respective metrics. Moreover, we integrate machine learning algorithms for distribution policies into the simulation framework in order to efficiently study distribution of content to the network nodes. These algorithms are pre-trained neural network models adapted for VANETs. Using these new functions, we can specify the simulation parameters, run a plethora of experiments and proceed to evaluate metrics and policies for content distribution.
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