Blockchain-Based Two-Layer Trusted Framework for Cold Chain IoT: Zero-Knowledge Authentication and Variational Autoencoder Anomaly Detection

Abstract

One of the most important properties of cold chain is that ensure that temperature-sensitive products such as food, medicine, and chemicals maintain quality and safety during transportation and storage. For traditional cold chain systems, most operations such as transportation and inspection were relying on manual inspection and decentralized systems, which are inefficient, error-prone, and lack transparency. Today, some studies have combined blockchain technology with the Internet of Things (IoT) to store various necessary supply chain data on the blockchain, thereby achieving the role of monitoring and review, providing a basic solution to these challenges. But there still some problems, for example, how to attribute the responsibility in the transportation process to individuals to achieve a precise accountability system? For example, know who is responsible for this leg of the shipment? who is responsible for receiving this shipment? Since the temperature and humidity data of the fruit may be constantly changing, how can you effectively detect whether these changes are justified so that you can respond effectively and in a timely manner to irregularities? Regarding above mentioned issues, in this paper, we propose a two-tier framework that combines biometric-based Zero Knowledge Proof (ZKP) authentication and AE-based AI anomaly detection. The authentication subsystem uses biometric data and personal information to generate credentials, which are verified by the ZKP stored on the chain. Meanwhile, the IoT device collects multisource sensor data processed by feature engineering, and detects temperature, humidity, and route anomalies via VAE model.