HCGN: A Hierarchical Causal-Graph Network for sustainable communication and coordination in edge–fog systems
Why this work is in the frame
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Bibliographic record
Abstract
In cloud computing systems, the proliferation of intelligent edge devices necessitates novel communication and coordination protocols that can operate under significant bandwidth and latency constraints. This necessity is driven not only by performance requirements but also by the growing imperative for sustainable computing, as inefficient communication is a primary driver of resources consumption in large-scale systems. This paper introduces the Hierarchical and Causal-Graph Network (HCGN), a framework designed for efficient, sustainable, and decentralized decision-making in large-scale edge computing environments. HCGN integrates a hierarchical control paradigm, mapping naturally to edge-fog architectures, with a Graph Neural Network (GNN) that learns a bandwidth-efficient communication policy between edge nodes. Furthermore, a novel Causal Credit Assignment Module (CCAM) enables intelligent and sustainable resource allocation by quantifying each node’s true causal contribution to system-wide objectives, ensuring that computational and communication resources are directed to the most effective parts of the network. We demonstrate through extensive simulations, including a novel edge-based collaborative video analytics task, that HCGN significantly outperforms traditional communication protocols in terms of task success rate, communication overhead, and robustness to network degradation. Our results validate HCGN as a scalable and resource-aware solution building the next generation of sustainable decentralized edge-fog-based systems.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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