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of smart technologies, notably the Internet of Things (IoT), has revolutionized numerous industry sectors.However, outdated IoT architectures constrain innovation in smart cities due to scalability issues, compromised resilience, and security vulnerabilities.This thesis scrutinizes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.iii This MASc thesis owes its existence to the guidance and encouragement of many.My appreciation goes to Dr. Chung-Horng Lung, whose keen insights sparked my interest in cloud technologies, one of the most fascinating focus areas of this research.I am also indebted to Mohammed Ismail, a long-time friend and academic cohort.His eagerness to explore the complexities of containerization technologies along with me, combined with his invaluable advice during the implementation and testing phases of my experiments, contributed significantly to the success of this work.My family, a constant source of strength, and my patient fiancé, who has endured my long working hours and has always stood by me, deserve heartfelt recognition.I would be remiss not to mention my two feline companions, whose incessant meows formed a harmonic and cheering soundtrack outside my office door.Finally, I attribute the greatest part of this journey's success to the unwavering guidance of my supervisor, Dr. Jason Jaskolka.His mentorship, from igniting my interest in cybersecurity to the detailed critiques of my work and consistent support, has been profoundly inspirational.His exemplary efficiency and dedication to his students' success were guiding lights during challenging times, for which I am endlessly grateful.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".