Survey of energy-efficient fog computing: Techniques and recent advances
Bibliographic record
Abstract
Undoubtedly, energy efficiency forms a fundamental pillar of the fog computing model. Processing data at the network's edge leads to a substantial reduction in energy consumption when contrasted with the alternative of transmitting all data to remote data centers, typically associated with cloud computing. This energy-saving approach not only promotes a more environmentally friendly footprint but also serves to prolong the operational life of battery-powered IoT devices, a particularly critical aspect, especially in remote or challenging-to-access environments. Thus, fog computing plays a crucial role in the operation of massive energy-saving IoT or green IoT networks. This study offers a comprehensive survey of recent research endeavors focused on achieving energy-efficient fog computing and eco-friendly fog computing solutions for IoT networks. The article initiates with an introductory overview of fog computing and subsequently delves into an in-depth exploration of various energy-conservation techniques tailored for fog computing environments. These techniques encompass energy-conscious architectural designs, data aggregation and compression strategies, low-power hardware implementations, energy-aware scheduling methods, task offloading mechanisms, resource utilization optimization, virtualization techniques, and energy harvesting approaches. In addition, this investigation introduces novel methodologies and outlines prospective research pathways to bolster the energy efficiency of fog computing. Moreover, practical applications are presented to highlight the potential advantages and obstacles associated with deploying energy-conscious strategies, providing insights into their effectiveness and practical implications in real-world scenarios. Essentially, this article can be considered a roadmap towards the realization of a sustainable fog computing ecosystem for extensive IoT networks. In addition, opens 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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".