The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

The popularity of the Internet of Things (IoT) and Industrial IoT (IIoT) has caused a rapid increase in connected entities and exposed its lack of adequate Identity Management (IdM). Traditional IdM systems are highly dependent on central authorities; any failure can potentially compromise the entire system. To eliminate the drawback of central authorities and evolve IdM systems to meet increasingly stringent security requirements, the Decentralized Identification approach has been proposed. This approach often relies on blockchain technology to provide a secure and tamper-proof method of managing and verifying identities. Therefore, this article investigates the capabilities of blockchain-based decentralized identification for the IoT domain, with an emphasis on the heterogeneity of online devices. It describes a number of features and specifications of decentralized identification with a specific focus on Self-Sovereign Identity (SSI), a model in which entities own their identities. The article concludes with a discussion of technical aspects as well as potential obstacles and constraints to the implementation of decentralized identification in the context of the Internet of Things.

The emergence of distributed ledger technologies in the vehicular applications' arena is decisively contributing to their improvement and shaping of the public opinion about their future. The Tangle is a technology at its infancy, but showing enormous potential to become a key solution by addressing several of the blockchain's limitations. This paper focuses on the use of the Tangle to improve the security of both in-vehicle and off-vehicle functions in vehicular applications. To this end, key operational performance parameters are identified, evaluated and discussed with emphasis on their limitations and potential impact in future vehicular applications.

Wireless communication technologies have become widely adopted, appearing in heterogeneous applications ranging from tracking victims, responders, and equipment in disaster scenarios to machine health monitoring in networked manufacturing industries. These systems are said to have real-time timeliness requirements since data communication must be conducted within predefined temporal bounds, whose unfulfillment may compromise the correct behavior of the system and cause economic losses or endanger human lives. The support of real-time communications over license-free bands in open environments is a challenging task since real-time medium access is only achieved by a strict timing control of all communicating stations (real and nonreal-time). However, in open communication environments, the traffic generated by uncontrolled stations cannot be avoided by existing medium access protocols. In this paper, the definition, implementation, and assessment of a novel MAC technique named bandjacking is performed. Results demonstrate that the support of low-power deterministic communications is possible in open environments by using bandjacking.

The imminent pervasive adoption of vehicular communication, either localized (ETSI ITS G5, IEEE WAVE or cellular D2D), long range (5G) or hybrid, will foster a richer service ecosystem for vehicular applications. The appearance of new cryptography-based solutions envisaging digital identity and currency exchange are set to stem new approaches for existing and future challenges. This article presents a novel tolling architecture that harnesses the availability of 5G C-V2X connectivity for open road tolling using smartphones, IOTA as the digital currency, and Hyperledger Indy for identity validation. An experimental feasibility analysis is used to validate the proposed architecture for secure, private, and convenient electronic toll payment.