Adaptive-Sensorless Monitoring of Shipping Containers

TL;DR

This paper introduces adaptive-sensorless models that correct systematic biases in predicting container conditions, significantly improving accuracy over traditional sensorless methods using a large dataset.

Contribution

The paper proposes the residual correction framework for adaptive-sensorless monitoring, integrating telemetry data to enhance prediction accuracy in shipping container monitoring.

Findings

Adaptive-sensorless models outperform baseline sensorless models in MAE and RMSE.

Models achieve MAEs of 2.24-2.31°C for temperature, outperforming 2.43°C.

Models achieve humidity MAEs of 5.72-7.09%, better than 7.99%.

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.