As the number of IoT devices grows, ensuring the secure validation and processing of the data they generate has become critical. This challenge becomes even more pronounced for mobile nodes, such as vehicles, which must continually reconnect to new Edge Servers while in motion. This work proposes a decentralized architecture for connecting IoT devices to Edge Servers, enabling secure data delivery to applications while minimizing overhead and ensuring trustworthy handovers between Edge Servers. A fundamental concern is the integrity of Edge Servers, as they may be compromised or exhibit malicious behavior. To address this, the proposed architecture relies on an external verifier service to continuously verify the integrity of Edge Servers. To demonstrate its feasibility, two prototypes were implemented using distinct consensus technologies and evaluated under realistic conditions. The first prototype, based on BFT-SMaRt, achieved lower latency and higher throughput but required dedicated, proprietary infrastructure and lacked global auditability. In contrast, the second prototype, leveraging an existing blockchain network, provides complete auditability and decentralization without proprietary infrastructure, though at the cost of higher latency. Experimental results confirm that both approaches deliver strong security guarantees, with trade-offs between performance and transparency, validating the architecture’s suitability for dynamic IoT environments.

• A novel stacked ensemble model that enhances predictive accuracy for solar irradiance, load, and energy prices. • A hierarchical recommendation heuristic integrating solar, battery, and grid energy sources for real-time cost optimization. • Promotion of the development of new research on renewable energy use and technological innovation. In recent years, the integration of solar power systems has seen substantial growth within smart houses. However, the inherent intermittency of solar irradiance introduces fluctuations in the available solar power, presenting a challenge for stable energy management. Developing and implementing precise recommendation systems based on solar power, energy market price, and weather forecasting methods become imperative in strategic planning and seamless energy management systems to address this issue effectively. This paper presents the A rtificial In T elligence-driven I oT RE comme NDE r E nergy System (ATIRENDEE) designed to enhance energy management in smart houses. Leveraging deep learning algorithms, a novel stacked ensemble model is developed to optimize energy consumption. Our results support the ATIRENDEE approach’s significant reduction in energy costs for smart homes, achieving an average monthly savings of over 45 % and reaching up to 68 % in specific cases. The multi-layered recommendation approach used enhances the efficiency and adaptability of the ATIRENDEE solution. By dynamically optimizing energy allocation among solar, battery, and grid sources, ATIRENDEE consistently outperformed static grid-based approaches, promoting cost-effective and sustainable energy use in eco-friendly smart houses or buildings.

The industrial control systems (ICS) are using Real-Time Ethernet (RTE) protocols for many years. Today, Ethernet based control systems are widely used in industries. The Time Sensitive Networking (TSN) initiative will definitely push their further diffusion. With the introduction of Industry 4.0, production machines and their components have been connected to the Internet. Currently adopted RTE protocols do not require authentication, and hence may exchange data also with potentially malicious partners. In this paper, a selective Denial of Service (DoS) attack is presented. The proposed Selective Queue Saturation Attack (SQSA) is aimed to jam the message queue of the RTE communication stack in selected devices. The SQSA minimizes the chances of being detected by keeping its requirements (in term generated traffic) as low as possible. The SQSA has been applied to a real scenario based on PROFINET. The results of the use case demonstrate: the feasibility of the proposed attack; the reduced footprint compared to known DoS attacks (more than one thousand times less); and the selectivity of the attack, which can disrupt the realtime behavior of even a single target node inside the RTE network.

The advantages of using communication networks to interconnect controllers and physical plants motivate the increasing number of networked control systems in industrial and critical infrastructure facilities. However, this integration also exposes such control systems to new threats, typical of the cyber domain. In this context, studies have been conducted, aiming to explore vulnerabilities and propose security solutions for cyber-physical systems. In this paper, a covert attack for service degradation is proposed, which is planned based on the intelligence gathered by another attack, herein proposed, referred as system identification attack. The simulation results demonstrate that the joint operation of the two attacks is capable to affect, in a covert and accurate way, the physical behavior of a system.