The Internet of Things (IoT) envisions a global market in which it would be possible to easily get data from IoT devices across the globe. However, the potential of this idea still needs to be unlocked. Centralized architectures fall short due to their lack of transparency and tendency to create silos. On the other hand, blockchain technology enables the creation of distributed and trustworthy systems, but its integration with the IoT is still a matter of research. IoT-based scenarios often employ numerous devices for the same sensing task, which may be heterogeneous and unreliable by purpose. In our vision, IoT applications should rely on data and its quality rather than on single providers. For this purpose, we propose an architecture that enables a decentralized IoT global market in which clients pay for data and device owners are rewarded for providing it. Our solution employs a distributed oracle layer on top of smart contracts powered by a distributed global network of IoT devices. The system supports IoT data source decoupling since the end-user can perform semantic queries bounded to specific locations and data types without specifying the target devices. In addition, it features automatic discovery, interoperability mechanisms, and reputation algorithms for the selection of trustworthy data sources. Our results show that the proposed system is robust and consistently provides quality data, even with multiple malicious data sources.

Assessing the integrity of industrial and civil appliances has become a priority worldwide. Noteworthy, this goal requires a strong synergy between multiple tools, disciplines, and approaches to be attained via a joint hardware-software co-design of the different Structural Health Monitoring (SHM) system components. This work proposes the $\sf{MAC4PRO}$ architecture, a sensor-to-cloud monitoring platform that seamlessly integrates sensing and software technologies for accurate data measurement, transmission, and analysis. The developed solution stands out for its interoperability and versatility, making it a promising candidate for integration in the next generation of smart structures. Our platform was validated during extensive experimental campaigns targeted at various industrial scenarios. The results show that the $\sf{MAC4PRO}$ architecture can identify subtle changes, such as 1mm size leakage events in pipeline circuits, or less than 1% frequency drifts in civil buildings after seismic excitation, while ensuring more than 90% reduction in the edge-to-cloud data transfer process.

The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects.

The rapid expansion of the Internet of Things (IoT) has led to significant data reliability and system transparency challenges, aggravated by the centralized nature of existing IoT architectures. This centralization often results in siloed data ecosystems, where interoperability issues and opaque data handling practices compromise both the utility and trustworthiness of IoT applications. To address these issues, we introduce ZONIA (Zero-trust Oracle Network for IoT Applications), a novel blockchain oracle system designed to enhance data integrity and decentralization in IoT environments. Unlike traditional approaches that rely on Trusted Execution Environments and centralized data sources, ZONIA utilizes a decentralized, zero-trust model that allows for anonymous participation and integrates multiple data sources to ensure fairness and reliability. This paper outlines ZONIA’s architecture, which supports semantic and geospatial queries, details its data reliability mechanisms, and presents a comprehensive evaluation demonstrating its scalability and resilience against data falsification and collusion attacks. Both analytical and experimental results demonstrate ZONIA’s scalability, showcasing its feasibility to handle an increasing number of nodes in the system under different system conditions and workloads. Furthermore, the implemented reputation mechanism significantly enhances data accuracy, maintaining high reliability even when 40% of nodes exhibit malicious behavior.