On leveraging Named Data Networking for vehicular and edge computing applications
Resumo
Vehicular and edge computing environments exhibit high mobility, intermittent connectivity, and rapidly changing network topologies, challenging traditional host-centric networking models. This thesis investigates Named Data Networking (NDN) as an enabling architectural approach to communication for realistic vehicular and edge computing applications in smart city scenarios. The work proposes NDN4IVC, a comprehensive experimental framework for NDN-based vehicular experimentation; NDN-Waze, a data-centric intelligent transportation system; and an NDN-based data offloading model for vehicular edge computing, including the Intelligent Edge Traffic Routing (iETR) architecture and the Data Mule Service Provider (DMSP) design. Simulation-based experiments under realistic mobility conditions show that NDN-based solutions improve robustness and data availability while reducing application-layer complexity. These results support the suitability of NDN as an architectural foundation for vehicular and edge computing systems.
Referências
Gong, T., Zhu, L., Yu, F. R., and Tang, T. (2023). Edge intelligence in intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 24(9):8919–8944.
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