Using an urban sensor network of Smart Lighting devices, it is possible to control the operation of the individual element of the public lighting system as well as to identify and prevent outages. Semantic-enhanced device/resource discovery will be adapted to reduce maintenance costs through intelligent techniques of predictive maintenance.

The expected impacts are:

• the minimization of recovery times in the event of a system failure;
• the reduction energy consumption and management costs of public lighting systems;
• the shrinking of the digital divide through real-time data sharing.

This scenario aims to monitor vehicles in a commercial fleet by installing electronic devices collecting significant information to optimize the intermodal transport of goods and passengers. Gathered data will be stored in a blockchain-based distributed database and processed with data mining and machine learning techniques pattern detection and business decision support.

The expected impacts are:

• the certification and aggregation of heterogeneous information from vehicles and context;
• the improvement of the detection of models through data mining techniques;
• the optimization of vehicle fleet management and the support for business decisions.

The aim of this scenario is to improve supply chain management and product traceability through a novel framework based on AI and blockchain technologies, characterized by automatic business rule execution and semantic-enhanced analytics. Applications on remote surgery, health and pharmacy sectors will be also considered.

The expected impacts are:

• the improvement of the traceability and the management of the supply chain;
• the increase in scalability of information systems for the supply chain;
• the maximization of customer satisfaction and products and processes safety specifically in the health sector.