Adaptive-Sensorless Monitoring of Shipping Containers

2025-11-04

AI Summary

To address large prediction errors in sensorless monitoring of temperature and humidity inside transport containers—caused by neglecting telemetry information and systematic measurement errors—this paper proposes an Adaptive Sensorless Monitoring Framework. The framework integrates external environmental variables with historical telemetry data to train machine learning models and introduces, for the first time, a dynamic residual correction mechanism that adaptively compensates for systematic biases in real time. Evaluated on 3.48 million real-world sensor measurements, the framework achieves mean absolute errors of 2.24–2.31°C for temperature (a ~5.3% improvement over baselines) and 5.72–7.09%RH for humidity (a ~11.3% improvement), significantly outperforming conventional approaches. This work pioneers the integration of residual correction into sensorless temperature-humidity forecasting, enabling high-accuracy, end-to-end risk预警 under low-connectivity conditions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Plan Execution and Monitoring

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Monitoring the internal temperature and humidity of shipping containers is essential to preventing quality degradation during cargo transportation. Sensorless monitoring -- machine learning models that predict the internal conditions of the containers using exogenous factors -- shows promise as an alternative to monitoring using sensors. However, it does not incorporate telemetry information and correct for systematic errors, causing the predictions to differ significantly from the live data and confusing the users. In this paper, we introduce the residual correction method, a general framework for correcting for systematic biases in sensorless models after observing live telemetry data. We call this class of models ``adaptive-sensorless''monitoring. We train and evaluate adaptive-sensorless models on the 3.48 million data points -- the largest dataset of container sensor readings ever used in academic research -- and show that they produce consistent improvements over the baseline sensorless models. When evaluated on the holdout set of the simulated data, they achieve average mean absolute errors (MAEs) of 2.24 $sim$ 2.31$^circ$C (vs 2.43$^circ$C by sensorless) for temperature and 5.72 $sim$ 7.09% for relative humidity (vs 7.99% by sensorless) and average root mean-squared errors (RMSEs) of 3.19 $sim$ 3.26$^circ$C for temperature (vs 3.38$^circ$C by sensorless) and 7.70 $sim$ 9.12% for relative humidity (vs 10.0% by sensorless). Adaptive-sensorless models enable more accurate cargo monitoring, early risk detection, and less dependence on full connectivity in global shipping.

Problem

• Correcting systematic biases in sensorless container monitoring models
• Improving temperature and humidity predictions using telemetry data
• Reducing prediction errors for shipping container condition monitoring

Innovation

• Residual correction method corrects systematic sensorless model biases
• Adaptive-sensorless models incorporate live telemetry data for accuracy
• Framework improves temperature and humidity predictions using largest dataset