Energy-Efficient Deep Learning Pipelines for Edge-Enabled Data Science Applications in IoT Networks
Bibliographic record
Abstract
The proliferation of Internet of Things (IoT) applications has heightened the demand for energy-efficient deep learning pipelines suitable for edge computing environments. Traditional deep learning models often struggle with high computational and energy costs, making them unsuitable for resource-constrained edge devices. This paper presents a holistic framework integrating lightweight model architectures, dynamic pruning strategies, adaptive data pre-processing, and real-time energy monitoring tailored for edge-enabled IoT networks. Experimental evaluations conducted using Raspberry Pi 4 and NVIDIA Jetson Nano across two public IoT datasets demonstrated up to 30% energy savings, a 20% reduction in inference latency, and only minor accuracy degradation. Our approach addresses both model-level and system-level optimizations, offering a sustainable solution for deploying deep learning models in real-world IoT scenarios. The findings contribute to advancing sustainable AI research and open avenues for future exploration in federated learning and hardware-aware model optimization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".