A Novel Method for Data Aggregation in Internet of Things (IoT) Networks Using Colored Petri Net (CPN) Modeling and Reinforcement Learning (RL)
Bibliographic record
Abstract
This paper proposes a novel method for data aggregation in Internet of Things (IoT) networks, utilizing Colored Petri Net (CPN) modeling and Reinforcement Learning (RL) to outperform traditional data aggregation techniques in terms of energy consumption, end-to-end delay, and network lifetime. Experimental results indicate that the proposed method achieves a 20-25% reduction in energy consumption, 15-20% lower end-to-end delay, and a 20-25% increase in network lifetime. These findings provide scalability and improved quality of service for resource-constrained IoT applications. Potential challenges include computational overhead, convergence time, and security concerns, necessitating further research. Future research directions involve integrating the method with edge computing, real-world deployment testing, developing hybrid algorithms, exploring adaptive reward functions, and assessing the environmental impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".