A trust‐based mechanism for drones in smart cities
Bibliographic record
Abstract
Smart cities equipped with intelligent devices can enhance the lifestyle and quality of humans by automatically and collaboratively acting as a sustainable resource to the ecosystem. In addition, the technological advancement can be further empowered by interconnecting various types of technologies, such as IoT, Artificial Intelligence, drones and robotics which will clearly improve the Quality of Services, energy efficiency and connectivity to the overall system. The integration of drones hovering over smart cities with the other devices in the smart city network brings a lot of benefits. However, it can also lead to various security and privacy concerns in the network. The aim of this article is to put forward a secure and safe smart city communication environment by proposing a trust establishment scheme for the ad hoc Unmanned Aerial Vehicles network. In which, malicious devices can be traced and blocked by analysing and evaluating their historical interactions within the system and calculating their trust values. A behaviour-based and local trust value scheme is used to analyse the trust of each communicating device that is further associated with a blockchain distributed ledger. The proposed mechanism is measured over various networking and security metrics, including throughput, latency, accuracy and block updating compared to the existing state-of-the-art solutions.
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".