Flexible Virtual Energy Sharing by Distributed Task Reallocation in IoT Edge Networks
Bibliographic record
Abstract
The convergence of Internet of Things (IoT) and edge computing provides a promising solution to many distributed IoT applications, which often involve real-time information gathering and complex processing. However, energy consumption of computational-intensive processing at edge devices becomes a main constraint due to limited battery capacity. In addressing this, computation offloading to a remote server has been utilized but leading to potentially increased latency due to network delay. In this paper, we propose a virtual energy sharing among edge IoT devices through a collaborative computing mechanism by a flexible and situation-dependent computation task reallocation coordinated by a smart gateway. Our key objective is to achieve energy aware task executions and energy sharing among collaborative edge devices by flexibly adjusting task volumes and CPU frequency based on the workload conditions. To implement this, trade-off between energy consumption and computation time of two collaborative edge devices is formulated to achieve a flexible and situation-dependent decision-making considering the time-varying resource conditions and application delay tolerance. Simulation results show that the proposed scheme could achieve a flexible virtual energy sharing by managing the trade-off between resource utilization and latency performance of tasks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".