A Secure IoT-Blockchain Framework for Vaccine Cold-Chain Monitoring With Predictive Analytics

The objective of this research is to design and test a framework for secure monitoring of a vaccine cold-chain with the help of Internet of Things (IoT) sensors, blockchain-based provenance verification, and predictive analytics.

The architecture of PharmaChain IoT Vault is an integrated system comprising low-power IoT telemetry acquisition, secure Message Queuing Telemetry Transport (MQTT)-based communication, integrity verification using SHA-256, a permissioned blockchain infrastructure, off-chain telemetry storage, and a forecasting module for transformers. A real-world vaccine cold-chain logistics (VCL) dataset of 26,674 observations was used for experimental evaluation, spanning 30 vaccine batches, 12 monitoring sites, and multiple distribution steps. To evaluate the feasibility of secure predictive monitoring under heterogeneous operational conditions, descriptive analysis and baseline forecasting evaluation, along with blockchain-enabled provenance verification, were performed.

There was a significant temporal variability in the dataset due to the changes in storage, transportation, and distribution conditions. Forecasting experiments using a baseline autoregressive model showed that it outperformed a persistence baseline, with lower mean absolute error (12.58°C) and root mean square error (15.15°C), thereby validating the use of vaccine logistics telemetry for predictive monitoring. The blockchain layer preserved the provenance of telemetry records in a tamper-evident manner by anchoring their SHA-256 hashes in the ledger, enabling cryptographic integrity verification.

It is shown that secure telemetry acquisition, blockchain-based provenance verification, and predictive analytics can be integrated into a single framework for vaccine cold-chain monitoring. The proposed architecture can be used to develop a system for trustworthy, intelligent management of cold chains and to illustrate the challenges of forecasting in a heterogeneous logistics environment. Future research will involve testing more complex forecasting models and testing the framework in operational conditions.

Not applicable. This study does not involve a clinical trial.