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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".