An efficient mobility-aware knowledge sharing framework for next-generation internet of vehicles

Vehicular Knowledge Networking, when integrated with Named Data Networking (NDN), unfold an emerging paradigm to deliver Knowledge—instead of raw data to the consumer. The mechanism improves the learning process and improves the decision-making capability of autonomous vehicles. However, in constantly evolving environment, adhering to the vanilla NDN forwarding mechanism fails to retain a reliable communication path between communicating vehicles. This results in frequent path disruptions, Knowledge retransmissions, resources utilization, and intolerable delays, ultimately hindering the timely delivery of Knowledge. To address these challenges, this article envisions MAKS—an efficient mobility-aware Knowledge sharing framework for next-generation Internet of autonomous Vehicles. MAKS incorporates vehicular dynamics and trajectory information to develop a Mobility-aware Forwarding Information Base (MaFIB), which ensures stable reverse paths and reliable Knowledge dissemination under varying network conditions. Additionally, MAKS presents efficient path recovery mechanisms that enables vehicles to carry-on Knowledge sharing in the event of potential forwarder failures, thus maintaining service continuity. Simulation results demonstrate that MAKS achieves an impressive Knowledge delivery ratio exceeding 90%, reduces path partitioning by more than 35%, and decreases the redundant retransmissions by approximately 40% compared to benchmark scheme.