A comprehensive survey of energy-efficient computing to enable sustainable massive IoT networks
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
Energy efficiency is a key area of research aimed at achieving sustainable and environmentally friendly networks. With the rise in data traffic and network congestion, IoT devices with limited computational power and energy resources face challenges in analyzing, processing, and storing data. To address this issue, computing technology has emerged as an effective means of conserving energy for IoT devices by providing high-performance computing capabilities and efficient storage to support data collection and processing. As such, energy-efficient computing, or "green computing," has become a focal point for researchers seeking to deploy large-scale IoT networks. This study provides a comprehensive Survey of recent research efforts aimed at achieving energy-efficient computing and green computing for IoT networks. To the best of our knowledge, none of the studies in the literature have discussed all types of green computing (edge, fog, cloud) and their role in enabling massive IoT networks in terms of energy efficiency. The article starts with an overview of computing technologies and then goes with a discussion of the empowering energy-saving techniques for computing (edge, fog, and cloud) environments including, energy-aware architecture, data aggregation and compression, low-power hardware, energy-aware scheduling, task offloading, switching on/off unused resources, virtualization, energy harvesting, and cooling optimization. This article is an outline of a roadmap toward realizing the vision of a sustainable computing environment for massive IoT networks; in addition, open the door for interested researchers to follow and continue the vision of Energy-Efficient Computing.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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