Strategic Data Offloading for 5G and Beyond for Internet of Vehicles Networks: Current Trends and Future Directions
Bibliographic record
Abstract
Mobility has long been a driving force behind human progress, continuously evolving alongside technological advancements. The proliferation of 5th-generation (5G) and the anticipated development of 6th-generation (6G) communication technologies, alongside advancements in artificial intelligence (AI), have significantly transformed the Internet of Vehicles (IoV) ecosystem. Despite these innovations, the computational demands of IoV applications have surged, placing substantial strain on backhaul network infrastructure and leading to increased service response times, computational costs, and energy consumption. To address these challenges, strategic data offloading for IoV has emerged as a critical research domain. Effective offloading mechanisms enhance computational efficiency by optimizing task distribution across network layers. However, determining what to offload, where to offload, and how to execute offloading optimally remains a pivotal challenge. This paper presents a comprehensive review of offloading strategies in 5G and beyond for IoV networks, systematically examining key decision-making principles, architectural frameworks, and optimization methodologies. Through an extensive comparative analysis, the most effective offloading strategies are identified, while their trade-offs in terms of latency, energy efficiency, scalability, and security are evaluated. Additionally, critical challenges are discussed, and future research directions essential for fostering greener and more intelligent IoV ecosystems are outlined. By advancing offloading techniques, this research demonstrates their role in enhancing the performance, sustainability, and adaptability of next-generation transportation systems, ultimately driving the evolution of smarter mobility solutions while improving traffic flow analysis and network responsiveness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".