Energy-Efficient Joint Optimization of Sensing and Computation in MEC-Assisted IoT Using Mean-Field Game
Bibliographic record
Abstract
Integrating multiaccess edge computing (MEC) with the Internet of Things (IoT) is able to provide IoT sufficient computational resources in addition to its capabilities of sensing and communication. In this article, given the limited computational and energy resources, IoT devices (IDs) are allowed to offload computational tasks to MEC servers for execution. However, as the number of IDs increases dramatically, jointly optimizing the usage of sensing, communication, and computational resources becomes challenging due to the exponential growth in interactions among the IDs. In this article, we address the energy-efficient joint optimization problem for sensing and computation in the MEC-assisted IoT system, aiming to ensure the freshness of the status update and minimize the energy consumption of IDs. To reduce the computation complexity, we introduce the concept of the general mean-field N-player Markov game (GMFG), and reformulate it as a mean-field game (MFG) with teams, leveraging the network structure of states. Considering the advantages of reinforcement learning (RL) for solving dynamic problems, we propose an MFG-based actor-critic algorithm (MFGAC) to minimize the long-term average system cost. Through extensive simulations, we demonstrate that the proposed method is effective and can outperform other schemes under different scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".