Toward Multicast NFV-Enabled IoT Frameworks: Game Theory for Mixed-AoAI
Bibliographic record
Abstract
In the context of multicast Network Function Virtual(NFV)-enabled Internet of Things (IoT), numerous sensor devices must efficiently transmit data to multiple data centers for real-time monitoring and analysis. Certain data centers require aggregated data from multiple sensors for informed decision-making. Outdated information lead to incorrect decisions, resulting in economic losses. Therefore, ensuring the timely and effective delivery of information is of paramount importance. However, the issue of information freshness in multicast networks has received limited attention. The deployment Virtual Network Functions (VNFs), data scheduling, and the multitude of routing possibilities pose significant challenges to studying information freshness. We introduce Mixed-Age of Aggregated Information (MAoAI) to quantify information freshness in multicast networks, integrating Age of Information (AoI) and Age of Aggregated Information (AoAI). To address this, we propose an optimization framework for coordinating multicast service requests. This framework takes into account VNF deployment and sharing, multicast routing, transmission scheduling, and data aggregation, mathematically formulated as a complex Integer Linear Programming (ILP) model. To tackle the scalability issue, we develop a Nash equilibrium-based Multicast and Scheduling Game (MSGame) approach, reducing CPU runtime by an average of 98.02% compared to ILP. Comprehensive simulations show improved solution quality and approximate optimal solutions with fewer iterations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".