Miguel L. Pardal

Diogo Vaz, David R. Matos, Miguel L. Pardal et al.

Modern distributed systems are supported by fault-tolerant algorithms, like Reliable Broadcast and Consensus, that assure the correct operation of the system even when some of the nodes of the system fail. However, the development of distributed algorithms is a manual and complex process, resulting in scientific papers that usually present a single algorithm or variations of existing ones. To automate the process of developing such algorithms, this work presents an intelligent agent that uses Reinforcement Learning to generate correct and efficient fault-tolerant distributed algorithms. We show that our approach is able to generate correct fault-tolerant Reliable Broadcast algorithms with the same performance of others available in the literature, in only 12,000 learning episodes.

João Miguel Guerreiro Fernandes, Samih Eisa, Miguel L. Pardal

From production to consumption, ensuring food quality and traceability depends on reliable monitoring of environmental conditions across the supply chain. Ambient sensing devices can collect relevant data such as temperature and humidity, but ensuring its integrity among stakeholders remains a challenge. This work presents AmBox, a system that enables device-to-blockchain ambient sensing for food traceability. AmBox connects sensors to a blockchain, ensuring secure, verifiable, and tamper-resistant data collection with minimal intermediaries. It manages sensor commissioning and operation with the adequate business context. AmBox can operate with standalone nodes or within a distributed node-mote architecture, allowing flexible deployment at different points along the supply chain. A prototype using Raspberry Pi and ESP32 hardware can record sensor data directly on Hyperledger Fabric. Experimental results show that AmBox provides timely and reliable data that can increase transparency and trust between the supply chain stakeholders.

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.