Sensor Calibration Model Balancing Accuracy, Real-time, and Efficiency

2025-11-10

AI Summary

Prior sensor calibration methods for edge devices evaluate models solely on macro-level metrics—accuracy, real-time performance, and resource efficiency—overlooking critical deployment bottlenecks such as instantaneous error and worst-case latency. To address this, we propose Scare, the first ultra-lightweight Transformer-based calibration model designed specifically for microcontrollers (MCUs). Scare pioneers a fine-grained decomposition of the three conventional objectives into eight quantifiable micro-requirements, enabling holistic co-optimization. Its core innovations include: (1) a Sequence Lens Projector (SLP) achieving logarithmic input compression; (2) Efficient Bitwise Attention (EBA), replacing multiplicative operations with binary hash-based bit operations; and (3) a hash-aware optimization strategy ensuring training stability and preservation of boundary information. Evaluated on large-scale air quality datasets and real MCU deployments, Scare consistently outperforms linear, hybrid, and deep learning baselines—and is the first calibration model to satisfy all eight micro-requirements.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Most on-device sensor calibration studies benchmark models only against three macroscopic requirements (i.e., accuracy, real-time, and resource efficiency), thereby hiding deployment bottlenecks such as instantaneous error and worst-case latency. We therefore decompose this triad into eight microscopic requirements and introduce Scare (Sensor Calibration model balancing Accuracy, Real-time, and Efficiency), an ultra-compressed transformer that fulfills them all. SCARE comprises three core components: (1) Sequence Lens Projector (SLP) that logarithmically compresses time-series data while preserving boundary information across bins, (2) Efficient Bitwise Attention (EBA) module that replaces costly multiplications with bitwise operations via binary hash codes, and (3) Hash optimization strategy that ensures stable training without auxiliary loss terms. Together, these components minimize computational overhead while maintaining high accuracy and compatibility with microcontroller units (MCUs). Extensive experiments on large-scale air-quality datasets and real microcontroller deployments demonstrate that Scare outperforms existing linear, hybrid, and deep-learning baselines, making Scare, to the best of our knowledge, the first model to meet all eight microscopic requirements simultaneously.

Problem

• Decomposes sensor calibration into eight microscopic requirements beyond macroscopic metrics
• Develops ultra-compressed transformer balancing accuracy, real-time performance and efficiency
• Enables deployment on microcontrollers while maintaining high accuracy and low overhead

Innovation

• Logarithmic compression preserves boundary information in bins
• Bitwise attention replaces multiplications with binary hash codes
• Hash optimization ensures stable training without auxiliary losses