Minimizing Age of Information in Multiaccess-Edge-Computing-Assisted IoT Networks
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Bibliographic record
Abstract
Internet of Things (IoT) applications, such as augmented/virtual reality, tactile Internet, immersive gaming, etc., are currently experiencing an unprecedented growth in their demand. IoT devices are constrained by limited computation and power features and might experience excessive computational latency to support resource-intensive tasks. Multiaccess edge computing (MEC) appears to be a promising solution in this regard to expedite the computations of resource-intensive tasks by offloading them to the edge of the network. This article considers a scenario where a base station (BS) serves traffic streams from multiple IoT devices. The packets from each stream arrive at the BS (following a stochastic process) and then forwarded to their respective destinations after they are processed by the MEC node. The scheduling decisions are aimed to keep the information fresh at the destination. The information freshness is captured by Age of Information (AoI) metric. We aim to minimize the expected sum AoI for the MEC-assisted IoT network and provide mathematically traceable expressions for the AoI. First, an optimization problem is formulated to find the optimal scheduling policy in order to minimize the expected sum AoI. The optimization problem is an integer linear programming (LP) problem, which is generally difficult to solve. Hence, we provide a simpler formulation of the problem and derive a more traceable expression for the expected sum AoI. With this approach, the joint impact of stochastic arrivals, scheduling policy, and unreliable channel conditions on the AoI is assessed. We also propose low-complexity algorithms to obtain results for larger networks. Finally, through extensive simulations, we demonstrate the effectiveness of our proposed methods as compared to other existing strategies in terms of achievable AoI.
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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 | 0.000 | 0.000 |
| 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