Optimizing Information Freshness in MEC-Assisted Status Update Systems With Heterogeneous Energy Harvesting Devices
Bibliographic record
Abstract
The ever-growing number of Internet-of-Things (IoT) devices makes multiaccess edge computing (MEC)-assisted status update system more and more attractive, which can be deployed to enable remote data acquisition and analysis from urban space. The ambient computing resource at edge automatically extracts valuable status update information from the data collected by IoT devices, which supports the real-time remote monitoring applications. In this article, we employ the concept of Age of Information (AoI) to quantify the freshness of status updates. To combat the limited battery capacity at IoT devices, energy harvesting (EH) is leveraged to capture the green energy from ambient environment. Specifically, we investigate an age minimization problem by considering the randomness in energy arrivals, heterogeneity in harvesting mode, and the stochasticity in transmission and computing process. The formulated problem is a long-term stochastic optimization problem. Then, we transform the original problem into a series of per-time slot deterministic optimization problem. An online scheduling policy is proposed to obtain the energy management decisions at devices, and the transmission and computing scheduling decisions among multiple devices without any prior knowledge on the network dynamics, which is facilitated to be implemented. Simulation results show that the performance of our proposed algorithm is competitive when compared with other existing schemes.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".