Smart contracts for certified operations in industrial distributed applications

The integration of smart contracts and blockchain technology is gaining momentum in industrial applications, especially in safety-critical systems such as predictive maintenance and real-time anomaly detection. These systems typically rely on sensor networks to collect data and stream it to cloud infrastructures for storage, analysis, and visualization. However, industrial applications pose unique challenges: they require sustainable storage solutions for long-term continuous monitoring, while ensuring data integrity and tamper-proof operations. At the same time, the performance of smart contracts in production-grade environments becomes critical, as industrial systems depend on them to handle high transaction volumes and maintain scalability under real-world conditions. The first section of this thesis presents a blockchain-based framework enabling certified data removal for continuous monitoring systems. Smart contracts define and run data retention policies, enabling the secure deletion of non-compliant data. This solution is applied to a real-world Structural Health Monitoring (SHM) use case, where the blockchain guarantees the tamper-proof deletion of data in a railway bridge monitoring application. In the second part, the focus shifts to the benchmarking of smart contracts in production-grade deployments. A step-by-step methodology is proposed for simulating real-world environments and evaluating smart contracts through key performance indicators such as Average Transaction Latency (ATL) and Average Transaction Throughput (ATT). The final aim is enabling organizations to make data-driven decisions regarding the introduction of smart contracts in industrial applications, evaluating various network constraints and blockchain configurations. Finally, these two contributions merge into an experimental study examining a production-grade deployment of the proposed smart contract using the discussed methodology. A framework for benchmarking is implemented to assist users in evaluating smart contracts automatically. The results show how the methodology enables organizations to make informed decisions regarding smart contract deployment and scalability through quantification of network limitations and blockchain configuration effects.