Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing
Large language models (LLMs) offer a natural-language interface for interpreting Internet of Things (IoT) sensor data in smart environments; however, cloud deployment introduces latency, privacy, and connectivity concerns. Local LLMs can reduce these limitations, but compact edge-deployable models often show weaker numerical reasoning when raw sensor readings are provided directly. This paper investigates whether prompt-side preprocessing can improve the accuracy-latency trade-off of local LLMs for environmental monitoring. We propose a structured prompt construction framework that transforms raw air-quality and thermal-comfort measurements into progressively enriched textual representati...
| Authors | Aygün Varol, Katarzyna Kołodziej, Łukasz Sobczak et al. |
|---|---|
| Primary category | cs.PF |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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