Emanuel Nunes

André Romão, Francisco Faria, João R. Matos et al.

Enterprise adoption of permissioned blockchains remains limited due to the complexity and cost of integrating legacy systems. We present a modular adapter architecture that bridges enterprise applications with blockchain networks, designed to support small and medium-sized enterprises with limited technical resources. The architecture provides five key modules: (1) configurable data extractors supporting diverse interfaces such as APIs and file uploads, (2) data transformers that can convert to standard formats, (3) messaging middleware to ensure operations can tolerate lack of connectivity and traffic spikes, (4) blockchain loader to commit transactions to the blockchain, and (5) status visibility to collect and expose runtime metrics that support operational transparency. We validated the adapters through a pilot deployment in a real-world fruit supply chain, involving three distinct enterprises. The pilot achieved blockchain integration with minimal workflow disruption, demonstrating the usefulness of these adapters for practical interoperability of existing systems with the blockchain.

Sara Aguincha, Emanuel Nunes, Samih Eisa et al.

Sensor technologies have evolved to a point where it is now practical to monitor products along the supply chain. The collected data can be stored in a decentralized way using blockchain technology. However, ensuring the reliability of the sensed data is a critical challenge. In other words, we need to trust the data that we write to the blockchain. In this work, we propose ChainGuards, a decentralized system that uses product-specific rules to verify data collected across the supply chain, with particular focus on sensor-derived information, issuing warnings and triggering audits when anomalies are detected. We evaluated ChainGuards using data from a real cherry supply chain deployment. The result shows that the implemented solution provides reliable verification of supply chain data with low performance overhead, able to correctly detect data discrepancies and inconsistencies.