A Secure IoT-Blockchain Framework for Vaccine Cold-Chain Monitoring With Predictive Analytics.
- 1Department of Computer Science and Engineering (AI&ML) Dayananda Sagar University Bengaluru South India.
- 2Faculty of Engineering and Technology Villa College Male Maldives.
Abstract
Objective
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.Methods
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.Results
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.Conclusions
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.Clinical trial registration
Not applicable. This study does not involve a clinical trial.
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BibTeX
@article{muthusamy2026secure,
title = {{A Secure IoT-Blockchain Framework for Vaccine Cold-Chain Monitoring With Predictive Analytics.}},
author = {Lakshmanan Muthusamy and Saravanakumar Sengottaiyan and Joshuva Arockia Dhanraj and Sriramkumar R},
journal = {Public health challenges},
year = {2026},
month = nov,
doi = {10.1002/puh2.70431},
url = {https://europepmc.org/article/MED/42968898},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Secure IoT-Blockchain Framework for Vaccine Cold-Chain Monitoring With Predictive Analytics.
AU - Lakshmanan Muthusamy
AU - Saravanakumar Sengottaiyan
AU - Joshuva Arockia Dhanraj
AU - Sriramkumar R
PY - 2026
DA - 2026/11/01
JO - Public health challenges
DO - 10.1002/puh2.70431
UR - https://europepmc.org/article/MED/42968898
AB - Objective 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.Methods 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.Results 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.Conclusions 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.Clinical trial registration Not applicable. This study does not involve a clinical trial.
SN - 2769-2450
ER -