Internet-of-Things (IoT) is an enabling technology for numerous initiatives worldwide such as manufacturing, smart cities, precision agriculture, and eHealth. The massive field data aggregation of distributed administered IoT devices allows new insights and actionable information for dynamic intelligent decision-making. In such distributed environments, data integrity, referring to reliability and consistency, is deemed insufficient and requires immediate facilitation. In this article, we introduce a distributed ledger (DLT)-based system for ensuring IoT data integrity which securely processes the aggregated field data. Its uniqueness lies in the embedded use of IOTA’s ledger, called “The Tangle”, used to transmit and store the data. Our approach shifts from a cloud-centric IoT system, where the Super nodes simply aggregate and push data to the cloud, to a node-centric system, where each Super node owns the data pushed in a distributed and decentralized database (i.e., the Tangle). The backend serves as a consumer of data and a provider of additional resources, such as administration panel, analytics, data marketplace, etc. The proposed implementation is highly modularand constitutes a significant contribution to the Open Source communities, regarding blockchain and IoT.

The upward trend of adopting Distributed Energy Resources (DER) reshapes the energy landscape and supports the transition towards a sustainable, carbon-free electricity system. The integration of Internet of Things (IoT) in Demand Response (DR) enables the transformation of energy flexibility, originated by electricity consumers/prosumers, into a valuable DER asset, thus placing them at the center of the electricity market. In this paper, it is shown how Local Energy Markets (LEM) act as a catalyst by providing a digital platform where the prosumers’ energy needs and offerings can be efficiently settled locally while minimizing the grid interaction. This paper showcases that the IoT technology, which enables control and coordination of numerous devices, further unleashes the flexibility potential of the distribution grid, offered as an energy service both to the LEM participants as well as the external grid. This is achieved by orchestrating the IoT devices through a Consumer Digital Twin (CDT), which facilitates the optimal adjustment of this flexibility according to the consumers’ thermal comfort level constraints and preferences. An integrated LEM-CDT platform is introduced, which comprises an optimal energy scheduler, accounts for the Renewable Energy System (RES) uncertainty, errors in load forecasting, Day-Ahead Market (DAM) feed in/out the tariff, and a fair price settling mechanism while considering user preferences. The results prove that IoT-enabled consumers’ participation in the energy markets through LEM is flexible, cost-efficient, and adaptive to the consumers’ comfort level while promoting both energy transition goals and social welfare. In particular, the paper showcases that the proposed algorithm increases the profits of LEM participants, lowers the corresponding operating costs, addresses efficiently the stochasticity of both energy demand and generation, and requires minimal computational resources.

Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject’s wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO₂) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information.

Accurate and timely detection of cardiac arrhythmias is crucial in reducing treatment times and, ultimately, preventing serious life-threatening complications, such as the incidence of a stroke. This becomes of major importance, especially during the diagnostic process, where there is limited access to cardiologists, such as in hospital emergency departments. The proposed lightweight solution uses a novel classifier, consistently designed and implemented, based on a 2D convolutional neural network (CNN) and properly optimized in terms of storage and computational complexity, thus making it suitable for deployment on edge devices capable of operating in hospital emergency departments, providing privacy, portability, and constant operation. The experiments on the MIT-BIH arrhythmia database, show that the proposed 2D-CNN obtains an overall accuracy of 95.3%, mean sensitivity of 95.27%, mean specificity of 98.82%, and a One-vs-Rest ROC-AUC score of 0.9934. Moreover, the results and metrics based on the NVIDIA^® Jetson Nano^™ platform show that the proposed method achieved excellent performance and speed, and would be particularly useful in the clinical practice for continuous real-time (RT) monitoring scenarios.

Nowadays in modern and ubiquitous computing environments, it is imperative more than ever the necessity for deployment of pervasive healthcare architectures into which the patient is the central point surrounded by different types of embedded and small computing devices, which measure sensitive physical indications, interacting with hospitals databases, allowing thus urgent medical response in occurrences of critical situations. Such environments must be developed satisfying the basic security requirements for real-time secure data communication, and protection of sensitive medical data and measurements, data integrity and confidentiality, and protection of the monitored patient's privacy. In this work, we argue that the MPEG-21 Intellectual Property Management and Protection (IPMP) components can be used in order to achieve protection of transmitted medical information and enhance patient's privacy, since there is selective and controlled access to medical data that sent toward the hospital's servers.