Carbon-Aware Edge Computing for Internet of Everything Networks: A Digital Twin Approach
Bibliographic record
Abstract
The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".