Decentralized Internet of Things (IoT):
The Internet of Things (IoT) is everywhere. Many sources forecast that, in few years, there will be billions of connected devices and many zettabytes (ZB) of data produced daily. However, often IoT-based ecosystems behave as closed islands, hardly interoperable with data produced and consumed by external sources. The IoT, in its collaborative form, aims to bridge the gap among such isolated systems through data integration, standards and decentralized architectures. This brings along many open issues such as open data integration, analysis, aggregation and classification, data inconsistency detection, and architectural problems in oracle-based IoT blockchain systems.
Digital Twins:
Digital Twins are virtual representations of physical objects, systems, or processes that are continuously updated using real-time data from sensors and connected devices. They enable monitoring, simulation, prediction, and optimization of real-world entities across domains such as manufacturing, smart cities, healthcare, and Industry 4.0. Their importance lies in improving efficiency, reducing operational costs, supporting predictive maintenance, and enabling data-driven decision making. Despite their potential, several open research challenges remain, including interoperability among heterogeneous systems, real-time synchronization, scalability, cybersecurity and privacy, as well as trustworthy AI-driven analytics for autonomous decision making.
Mobile Crowdsensing (MCS):
In order to gather data about a poorly monitored environment, often many rely on opportunistic sources such as mobile phones. Users joining a Mobile Crowdsensing (MCS) campaign provide their sensor data without being actively involved. For such scenarios, online data analysis and distributed algorithms are a crucial component, since data inconsistency and redundancy have to be avoided, while a sufficient coverage as well as a low effort for the users have to be ensured.
