International Journal of Science, Engineering and TechnologyAuthors: Rashmi Vanajakar
Abstract: The Internet of Vehicles is being recast around two technologies that are reaching deployment maturity in the same window. On the cellular side, sixth-generation networks open terahertz spectrum, integrated sensing and communication, and a space-air-ground integrated fabric in which roadside units, unmanned aerial vehicles, and low-earth-orbit satellites all serve as edge servers. Digital twin networks add a continuously synchronised virtual counterpart for every vehicle, road segment, and radio environment, on which learning algorithms can operate as if it were the physical network itself. Each technology has a substantial literature on its own; the joint deployment they are now becoming raises four questions that no single paper resolves: how a radio-frequency twin is grounded in the physics of a millimetre-wave channel, how vehicle twins migrate across heterogeneous edge servers, how learning is split between vehicles and their twins without leaking data, and how the resulting pipeline is defended against active falsification. This review reads fifteen recent peer-reviewed frameworks against those questions and treats them as components of a single deployed pipeline rather than as isolated proposals. The 3D ray-tracing RF digital twin scheme of Liu et al. is given a dedicated discussion because most of the learning-oriented works reviewed here rest on a channel abstraction that, in deployment, would have to be served by some form of RF twin. Two comparison tables consolidate the readings, and five research gaps are identified: unverifiable twin fidelity, fragmented benchmark practice, opaque synchronisation cost, weak active-adversary threat models, and absent end-to-end energy accounting. Each gap is paired with an incremental, testable next step.
DOI: http://doi.org/10.5281/zenodo.20557590
