Towards Enhanced Urban Management: Introducing A Model for Autonomic Smart City Management
Bibliographic record
Abstract
A smart city leverages the Internet of Things (IoT) to enhance citizens' lives. Real-time management of massive data is a challenge. Smart cities are complex and interconnected, requiring interdisciplinary collaboration for urban planning, IoT, and monitoring. This paper proposes a novel Model for Autonomic Smart City Management, aimed at the comprehensive management of diverse urban aspects, including environmental conditions and multilayered infrastructure. Emphasizing continuous monitoring, data analysis, and realtime resource assessment, the system employs the MAPE-K approach for automated data processing, with the aim of reducing the need for human intervention. A novel aspect of the model is that it leverages and integrates existing IoT and monitoring platforms. The model introduces algorithms and policies with the aim of automating management activities and operations. The primary goal is to enhance urban efficiency, sustainability, and residents' quality of life while minimizing manual efforts and ensuring long-term cost savings.
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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".