From Vulnerability to Resilience: Securing Public Safety GPS and Location Services with Smart Radio, Blockchain, and AI-Driven Adaptability
Bibliographic record
Abstract
In an era where public safety hinges on real-time intelligence and rapid response, this paper delves into the pivotal role of location-based services (LBSs) in empowering law enforcement and fire rescue operations. GPS tracking systems have revolutionized situational awareness and resource management, yet they come with critical security and privacy challenges, including unauthorized access, real-time data interception, and insider threats. To address these vulnerabilities, this study introduces an innovative framework that combines blockchain, artificial intelligence (AI), and IoT technologies to redefine emergency management and public safety systems. Voice-command virtual assistants powered by AI enable hands-free operations, enhance hazard detection, and optimize resource allocation in real time, while blockchain’s decentralized and tamper-proof architecture ensures data integrity and security. By integrating these cutting-edge technologies, the research showcases a system design that not only secures sensitive information but also drives operational efficiency and resilience. With applications spanning smart cities, autonomous systems, and fire rescue operations, this study offers a transformative vision for public safety, emphasizing technology integration, digital innovation, and trust-building. These advancements promise not only to protect responders and communities but also to redefine the standards of security and efficiency in modern emergency management.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".