6G-Enabled IoT for Next-Generation Vehicular Communication
Bibliographic record
Abstract
The rapid evolution of sixth-generation (6G) wireless communication is poised to transform next-generation vehicular networks by enabling highly reliable, intelligent, and ultra-low-latency connectivity. This article investigates the core technological foundations of 6G—including terahertz (THz) communication, massive MIMO, reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and URLLC+—and explains how they collectively strengthen the Internet of Vehicles (IoV) ecosystem. The study provides a comprehensive analysis of network slicing as a key enabler for supporting heterogeneous vehicular applications ranging from safety-critical services to infotainment systems. Scalability challenges in dense vehicular environments are addressed through techniques such as AI-driven spectrum management, interference mitigation, and RIS-assisted coverage enhancement. Additionally, the article highlights green IoT strategies for minimizing energy consumption through task offloading, edge computing, and renewable-energy-powered infrastructures. A performance analysis using synthetic datasets demonstrates realistic trends in latency, reliability, spectral efficiency, slice utilization, and energy consumption. Results illustrate the significant improvements achievable with RIS, THz bands, and edge-enabled optimization. Overall, this work provides a unified overview of how 6G and IoT technologies will reshape autonomous transportation systems, enabling safer, more sustainable, and highly efficient vehicular communication networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".