Enabling Cloud-Level Accuracy in Edge AI through IoT Data Preprocessing

Researchers demonstrate that preprocessing raw IoT sensor data into structured textual formats significantly improves the accuracy of edge-deployed language models for environmental monitoring, narrowing the performance gap with cloud-based systems while maintaining low latency. Testing on indoor and outdoor air-quality datasets shows local model accuracy improving from 50.9% to 81.7% indoors and 63.7% to 89.3% outdoors through progressive prompt enrichment, achieving inference speeds near 0.22 seconds.

This research addresses a critical infrastructure challenge in edge computing: deploying machine learning models locally without sacrificing accuracy or requiring constant cloud connectivity. The study demonstrates that the problem isn't inherently tied to model capability but rather how data is presented to the model. By structuring raw sensor readings into enriched textual representations—progressing from raw values to threshold-aware descriptions to summary flags—researchers achieved substantial accuracy improvements that make local inference practically viable.

The work emerges from growing recognition that edge AI deployment solves real problems: latency matters for real-time environmental monitoring, privacy concerns affect IoT systems handling sensitive building data, and connectivity cannot be assumed reliable in all deployment scenarios. Previous approaches assumed either accepting lower accuracy locally or accepting the costs of cloud deployment. This research suggests a third path: intelligent data preprocessing can substantially reduce this trade-off.

The practical implications extend beyond academic interest. Smart building systems, environmental monitoring networks, and industrial IoT applications represent substantial market segments where reducing cloud dependency while maintaining accuracy creates value. The inference latency of 0.22 seconds makes real-time decision-making feasible. Organizations deploying IoT systems can implement this approach using existing hardware (Raspberry Pi) and open-source language models, reducing operational costs and improving system resilience.

Future work should explore whether these preprocessing principles transfer to other sensor types and domains, how preprocessing complexity scales with model size, and whether the approach generalizes to more complex decision tasks beyond binary classification. The framework's simplicity suggests broad applicability across IoT verticals.

• →Prompt-side data preprocessing improved local LLM accuracy by up to 30 percentage points, making edge deployment practically competitive with cloud systems.
• →Local inference achieved mean latency of 0.22 seconds without chain-of-thought prompting, enabling real-time environmental monitoring applications.
• →The structured prompt framework transforms raw sensor data through three enrichment levels, demonstrating that model accuracy depends significantly on input representation.
• →Testing across five local and five cloud LLMs shows results generalize beyond single model implementations, suggesting robust methodology.
• →The approach enables privacy-preserving, offline-capable IoT analytics using commodity hardware like Raspberry Pi with minimal computational overhead.