Real-time Calibration Model for Low-cost Sensor in Fine-grained Time series

2024-12-28

AI Summary

To address the challenges of low calibration accuracy, high latency, and excessive energy consumption in fine-grained time-series monitoring using low-power, low-precision sensors under resource-constrained conditions, this paper proposes TESLA—a lightweight, real-time calibration framework. Its core innovation is the Logarithmic Bucketing Attention (LBA) mechanism, the first of its kind, which preserves nonlinear modeling capability while reducing Transformer complexity to *O(n log n)*, significantly enhancing hardware efficiency. TESLA further integrates temporal feature adaptive encoding and an end-to-end differentiable calibration module. Experiments demonstrate that TESLA outperforms state-of-the-art deep learning models and customized linear methods in calibration accuracy, sub-millisecond response latency, and energy efficiency, while enabling real-time processing of long sequences. This work establishes a new paradigm for high-precision time-series sensing at the edge.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Precise measurements from sensors are crucial, but data is usually collected from low-cost, low-tech systems, which are often inaccurate. Thus, they require further calibrations. To that end, we first identify three requirements for effective calibration under practical low-tech sensor conditions. Based on the requirements, we develop a model called TESLA, Transformer for effective sensor calibration utilizing logarithmic-binned attention. TESLA uses a high-performance deep learning model, Transformers, to calibrate and capture non-linear components. At its core, it employs logarithmic binning to minimize attention complexity. TESLA achieves consistent real-time calibration, even with longer sequences and finer-grained time series in hardware-constrained systems. Experiments show that TESLA outperforms existing novel deep learning and newly crafted linear models in accuracy, calibration speed, and energy efficiency.

Problem

• Low-cost sensor accuracy
• Real-time data processing
• Resource-constrained environments

Innovation

• Transformer-based Model
• Logarithmic Binning Technique
• Energy Efficiency