An Overview on Intelligent Edge Computing for Enhancing CAEV and UAV Charging in 6G ITS
Bibliographic record
Abstract
6G networks, characterized by ultra-low latency and ubiquitous computing, herald a new era where Connected and Autonomous Electric Vehicles (CAEVs) and Unmanned Aerial Vehicles (UAVs) are redefining smart mobility. Equipped with real-time data processing, Artificial Intelligence (AI), and seamless connectivity, these vehicles seek efficient charging solutions facilitated by Intelligent Edge Computing (IEC). IEC, through latency reduction and optimized charging processes, promises rapid charging, grid stability, enhanced security, and renewable energy integration for intelligent transportation systems (ITS). This survey comprehensively examines IEC techniques and architectures, uncovering their impact on charging efficiency, security, and reliability within smart mobility frameworks. Through a review of research papers, the survey provides insights into real-world applications and IEC advancements, revealing key challenges and emerging research directions. The survey envisions an efficient, secure, and interconnected 6G-era charging ecosystem, with future directions including ultra-fast charging, renewables integration, enhanced security, standardization, AV-V2X synergy, predictive maintenance, and blockchain transparency, fundamentally reshaping AI-driven smart mobility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".