QoS Performance Measurement Through SRv6 Network Programming for Smart City IoT Traffic
Bibliographic record
Abstract
Segment Routing over IPv6, also known as SRv6, is a modern networking solution that aims to improve the current Internet of Things (IoT) network's reliability, availability, and scalability.Performance measures are required to evaluate SRv6 behaviors or functions.The proposed work aims to provide real IoT traffic profiles to assess the performance of SRv6 behaviors.In particular, a three-module SRv6 programming model is proposed to measure the performance of SRv6 policy headend and endpoint behavior and ensure reliability and quality of service (QoS).Moreover, a novel finder algorithm for maximum receive rate (MRR) benchmarking is proposed, which can outperform existing techniques in terms of throughput/bandwidth performance while maintaining the same computational resources.Finally, implementation results provide insights into forwarding different IoT use-cases traffic based on the functional service requirements.That also ensures a higher usage level of existing IoT networks, minimizing the need for additional capacity and lowering network costs.I want to express my special gratitude to my supervisor, Prof. Mohamed Ibnkahla, who allowed me to become a part of the Sensor Systems and Internet of Things (IoT) laboratory.I am incredibly grateful to him for providing me with academic and generous financial support throughout the road to finishing this thesis.I would also like to extend my warm thanks to Dr. Abdelrahman Eldosouky for his excellent suggestions and advice that have helped me to improve the quality of the thesis.My utmost thanks go to all the colleagues in our lab
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".