An ICN-based publish-subscribe platform to deliver UAV service in smart cities
Bibliographic record
Abstract
Intelligent Transportation Systems (ITS) play significant role in the management of traffic congestion in large cities. However, current ITS platforms are not suitable for real-time traffic control mainly due to the facts that 1) the contents are not rich enough to provide detailed information of current transportation network state, 2) there is no sophisticated notification system to alert the ITS platform about major issues in real-time. Unmanned Autonomous Vehicles (UAV) are promising candidates to provide rich content for traffic control systems in large cities and enable real-time notifications, especially when deployed on a platform that is content-oriented. This paper presents a sensor as a service platform to host live content streams (video, data) from a diverse set of input streams including UAVs, city cameras, loop detectors, etc., and to make the data available to a broad range of customers using a novel data dissemination layer. The data-dissemination layer is a content-oriented system based on information-centric networking, a new paradigm that puts content first, and which inherently enables content mobility and content security (through encryption on demand). To support real-time notification, we have implemented publish/subscribe overlay system based on the ICN paradigm. have also conducted live demos with UAVs providing live transportation video data in the system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".