Proactive Public Safety in 6G: Leveraging AI and Crowdsourcing for Critical Tasks
Bibliographic record
Abstract
Sixth Generation (6G) networks will create new possibilities to enhance public safety and security standards. The implementation of public safety operations and mission-critical applications requires ultra-fast and resilient communication through uninterrupted heterogeneous networks which integrate terrestrial, aerial and satellite systems. The real-time processing of massive data, decision-making and resource allocation at the edge faces multiple challenges during immediate public safety response scenarios. This article identifies specific obstacles while presenting a new framework which combines crowd-sourced data with advanced Artificial Intelligence (AI) algorithms to forecast situations needing public safety intervention. Crowd-sourced data is collected from social media activities, mobile devices, digital twins of users in the metaverse, autonomous self-driving vehicles, and data generated from Internet of Everything (IoE) devices. The framework uses blockchain technology to authenticate and verify collected information. Transformer models, generative AI, and Federated Learning (FL) collectively enhance real-time event predictions and enable dynamic resource allocation at the edge. The allocation of communication and edge computing resources to higher-risk areas through cooperative edge devices such as Unmanned Aerial Vehicles (UAVs) ultimately enables seamless communication and efficient resource allocation for critical tasks.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".