Emerging AI and 6G-Based User Localization Technologies for Emergencies and Disasters
Bibliographic record
Abstract
This paper presents state-of-the-art user localization technologies that can effectively assist the authorities and first responders to localize individuals during emergency and disaster scenarios. Localization is an essential tool that ensures preparedness, response, and coordination between first responders and impacted people. Many of these technologies employ artificial intelligence techniques to improve the localization accuracy using the information collected by a variety of sensing equipment such as sensors, unmanned aerial vehicles, wireless access points, cameras, and smart load meters. The paper also highlights the emerging sixth-generation (6G) wireless technologies such as integrated sensing and localization, THz communications, satellite-based non-terrestrial networks, and reconfigurable intelligent surfaces that can modify the reflection properties of materials to enable device localization. These technologies are expected to play a key role in enabling emergency services. Other localization technologies include WiFi, Bluetooth, radio frequency identification, and long range wide area network. In addition, crowd sensing and smart meter-based non-intrusive load monitoring (NILM) techniques that are supported by deep learning and federated learning techniques are also presented in the context of device localization. To the best of our knowledge, this paper introduces NILM for the first time as a useful technique that can provide information about the inhabitants’ behaviour in residential and commercial buildings to assist in emergency and public safety applications. All the technologies reviewed in this paper can play an effective role in device localization, and are presented to serve as a foundation for researchers to further investigate and conduct research in this domain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".