Smart and Green Mobility Management for 5G‐enabled Vehicular Networks
Bibliographic record
Abstract
Summary With the recent demand of sustainable and green smart cities, we are witnessing a growing research initiative toward the development of efficient energy‐aware 5G/Wifi‐6 wireless networks. This has led to the development of what is referred to as the Internet of Energy‐based technology to support heterogeneous and complex wireless systems such as intelligent vehicular network and smart connected cities as well as efficiently manage their available energy resources. Towards this end, the next generation of 5G/Wifi‐6 wireless network technologies shall provide a practical platform to support green Internet of vehicular networks, smart transportation systems, and smart cities. In this context, the management of vehicles' mobility and communication protocol needs to be fully investigated, adapted and reconfigured to better fit the power consumption and the energy resources' limitations of the next generation of intelligent vehicular networks. In this article, we present the latest mobility management protocols designed for 5G‐enabled vehicular networks, discuss their efficiency, their design, and their drawbacks. We point out the main characteristics, components, and limitation of mobility management protocols and wireless access. Last, but not least, we discuss several open issues, followed by future research directions toward the design and development of mobility management schemes for the next generation of green 5G and beyond enabled vehicular 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".