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About

Salvatore Andolina is a researcher whose work sits at the intersection of cloud computing, edge intelligence, and industrial robotics. His primary research areas include predictive analytics, fog and cloud-to-edge architectures, and human-robot interaction. Andolina’s major contributions lie in designing flexible, modular platforms that enable smart predictive maintenance in manufacturing. His 2020 paper, "A Cloud-to-Edge Approach to Support Predictive Analytics in Robotics Industry," with 46 citations, proposes a framework that processes data across cloud and edge layers to detect anomalies and predict failures in cyber-physical systems. He further advanced this work with a microservice architecture for predictive analytics, cited 26 times, and a fog computing approach for predictive maintenance, cited 17 times. Notably, Andolina also explored the human side of robotics, designing multi-touch interfaces for multi-robot path planning and control, and addressing the transformation of robotics education during the COVID-19 pandemic. His contributions are shaping how industries leverage distributed computing for real-time, proactive decision-making, making him a key figure in the evolution of smart manufacturing and human-robot collaboration.