Power Efficient Networking Support for Digital Twins with Age of Information Targets
Bibliographic record
Abstract
This paper studies network resource allocation for multiple IoT devices as physical systems (PSs) tasked with maintaining their digital twins (DTs) at a shared edge server (ES) through a shared communication channel. The problem is formulated as a constrained Markov decision process with an objective of reducing the average transmission power of the PSs while keeping the age of information (Aol) at the DTs below predetermined targets. A hybrid decision making frame is proposed, where multiple agent reinforcement learning is used to make decisions for the transmission power of the PSs in a distributed way, and a centralized and deterministic algorithm is proposed to allocate the computation resources of the ES among the DTs. Simulation results show that, compared with the multiagent duelling double deep Q-Network, the proposed multi-agent deep deterministic policy gradient for power allocation together with the urgency-baed computation resource allocation solution achieves much lower the average power consumption of the PSs while maintaining low AoI violation rate at the DTs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".