Energy and Time-Effective Computation Offloading for Edge Computing-Enabled IoT Networks
Bibliographic record
Abstract
By connecting and integrating diverse devices over a wireless connection, the Internet of Things (IoT) has revolutionized various domains and environments. However, IoT nodes' constrained battery and processing capacity limit their performance, making computation offloading a viable solution. This solution enables the migration of high-demand applications from IoT nodes to the Edge. This process depends on several variables, including the computing power available at IoT nodes, the accessibility of nearby Edge resources, and the connectivity condition between IoT nodes and the Edge. IoT brings several challenges to the application of computation offloading, including the heterogeneity of IoT and the limited resources of its nodes. To this end, we consider energy consumption and execution time measurements of different IoT applications running on physical IoT sensor nodes. Based on these measurements, we propose adaptive schemes that consider the resources available at the IoT nodes, as well as the number and type of available Edge servers to meet the requirements of IoT applications. Finally, we conduct extensive experiments to analyze the performance of the proposed adaptive schemes against other baseline schemes. We also study these adaptive schemes in a network scenario, where we schedule IoT applications based on the network status.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".