A Novel Method for Data Aggregation in Internet of Things (IoT) Networks Using Colored Petri Net (CPN) Modeling and Reinforcement Learning (RL)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it