Harmony or Involution: Game Inspiring Age-of-Information Optimization for Edge Data Gathering in Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Age-of-Information (AoI) has been recently reckoned as a suitable parameter to evaluate the freshness of collected information, which is essential for data retrieval in Internet of Things, especially the monitoring tasks, e.g., the operating situation of equipments. To motivate a large number of sensor nodes and solicit more up-to-date information from these nodes, the control center usually allocates rewards to nodes according to their proportional contributions. This induces intense competitions among nodes who try to gain high payoffs by carefully balancing the rewards and the costs. In this article, we propose a novel stochastic game model to formulate the competition among sensor nodes, which considers AoI as a metric used by the control center to quantify the contributions of nodes. We also take into account the uncertainty of channel quality, which affects the transmission success ratio of packets generated by nodes. Finally, we design an ϵ-Nash learning algorithm, which adopts the θ-greedy exploration strategy, to derive the ϵ-approximate Nash equilibrium such that nodes can maximize their long-term payoffs. Our substantive simulation results and analysis verify that the proposed algorithm outperforms baseline algorithms in bringing higher payoffs to nodes and more fresh information to the control center.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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