Edge-Based Platform for Large Language Models Deployment in Internet of Things Applications
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
Decision-making support systems are a growing trend in Internet of Things (IoT)-based applications. It is due to the introduction of Large Language Model (LLM) deployment platforms. However, cloud-centric approaches struggle with their implementation due to scalability issues. This work proposes an edge-based alternative to improve the scalability of LLM deployment in IoT-based applications. It also presents the incorporation of Raspberry Pis with low-cost, compact, and trending Artificial Intelligence (AI)-based applications, making it an ideal choice as an IoT device for deploying the LLM locally. Experiments evaluate the responsiveness of the LLM under the proposed edge-based platform. The results show its potential in minimizing the likelihood of network scalability issues, while the edge-based approach manages the LLM locally through the Raspberry Pi.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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