A Scalable Approach to Improve Security and Resilience of Smart City IoT Architectures
Bibliographic record
Abstract
The swift emergence of smart technologies, notably the Internet of Things (IoT), has revolutionized numerous industry sectors. However, outdated IoT architectures constrain innovation in smart cities due to scalability issues, compromised resilience, and security vulnerabilities. This thesis scrutinizes these challenges, advocating a contemporary approach to IoT system design. Prioritizing performance, scalability, security, and resilience, the research delves into the applicability of Platform-as-a-Service (PaaS) and Infrastructure-as-Code (IaC) methodologies, endorsing containerization and cloud-centric patterns for smart city IoT. The introduced model is contrasted with prevailing architectures, highlighting a trajectory for IoT progression. Merging empirical and theoretical insights, this study furnishes guidelines for the future of IoT in smart industries, underscoring the benefits of service-based architectures for efficiency, resilience, and security.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".