Performance Evaluation of Fog Computing for Latency and Energy Efficiency in IoT Applications
Bibliographic record
Abstract
A simulation through iFogSim evaluates the performance between cloud-only and edge-ward placement strategies in Fog computing environments when used for traffic surveillance and an online multiplayer game. An evaluation operated via iFogSim framework conducts tests on essential metrics, which include latency and energy consumption alongside network usage, RAM usage, and data transfer rate measurements. Our study establishes that using edge-ward strategy leads to substantial performance improvement through minimal latency while decreasing both energy consumption and network usage particularly when Fog devices operate at network edges during the traffic surveillance scenario. The edge-ward strategy delivers better scalability with shorter control loop delays which delivers enhanced performance for users in the analysis of the online game case study. On the other hand, the fog-based strategy maintains steady performance improvements for RAM utilization through enlarged deployment numbers combined with sustained minimal overhead. Fog computing demonstrates strong potential for improving the performance alongside resource usage in both IoT applications and latency-centric implementations thus serving as a viable solution for solving traditional cloud-based system constraints.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".