The rise in electricity costs for households over the past year has driven significant changes in energy usage patterns, with many residents adopting smarter energy-efficient practices, such as improved indoor insulation and advanced home energy management systems powered by IoT and Digital Twin technologies. These measures not only mitigate rising bills but also ensure optimized thermal comfort and sustainability in typical residential settings. This paper proposes an innovative framework to facilitate the adoption of energy-efficient practices in households by leveraging the integration of Internet of Things technologies with Digital Twins. It introduces a novel approach that exploits standardized parametric 3D models, enabling the efficient simulation and optimization of home energy systems. This design significantly reduces deployment complexity, enhances scalability, and empowers users with real-time insights into energy consumption, indoor conditions, and actionable strategies for sustainable energy management. The results showcase that the proposed method significantly outperforms traditional approaches, achieving a 94% reduction in deployment time and a 98% decrease in memory usage through the use of standardized parametric models and plug-and-play IoT integration.

Low-power embedded systems have been widely used in a variety of applications, allowing devices to efficiently collect and exchange data while minimizing energy consumption. However, the lack of extensive maintenance procedures designed specifically for low-power systems, coupled with constraints on anticipating faults and monitoring capacities, presents notable difficulties and intricacies in identifying failures and customized reaction mechanisms. The proposed approach seeks to address the gaps in current resource management frameworks and maintenance protocols for low-power embedded systems. Furthermore, this paper offers a trilateral framework that provides periodic prescriptions to stakeholders, a periodic control mechanism for automated actions and messages to prevent breakdowns, and a backup AI malfunction detection module to prevent the system from accessing any stress points. To evaluate the AI malfunction detection module approach, three novel autonomous embedded systems based on different ARM Cortex cores have been specifically designed and developed. Real-life results obtained from the testing of the proposed AI malfunction detection module in the developed embedded systems demonstrated outstanding performance, with metrics consistently exceeding 98%. This affirms the efficacy and reliability of the developed approach in enhancing the fault tolerance and maintenance capabilities of low-power embedded systems.

The efficient management of IoT systems is fundamental to advancing smart cities while enabling the seamless integration of technologies that enhance urban sustainability and resilience. This paper introduces a Hierarchical Resource Management System (HRMS) tailored for IoT-enabled smart cities, emphasizing a decentralized architecture at the building level and scaling up to city-wide applications. At its core, the system integrates the Adaptive Resilient Node (ARN), designed to autonomously manage energy resources and ensure continuous operation through self-healing capabilities. This study outlines the HRMS architecture, operational workflows, and core functionalities, demonstrating how the hierarchical framework supports real-time decision-making, fault tolerance, and scalable resource allocation. The proposed system’s lightweight inter-node communication enhances workload balancing and system responsiveness, addressing critical challenges in urban energy management. Experimental evaluations show that the system achieves up to a 50% improvement in energy efficiency and a 30% reduction in downtime across various urban environments, highlighting its transformative potential for sustainable and resilient urban growth.

THe energy-efficient operation of microgrids—a localized grouping of consuming loads (domestic appliances, EVs, etc.) with distributed energy sources such as solar photovoltaic panels—suggests the deployment of Energy Management Systems (EMSs) that enable the actuation of controllable microgrid loads coupled with Artificial Intelligence (AI) tools. Such tools are capable of optimizing the aggregated performance of the microgrid in an automated manner, based on an extensive network of Advanced Metering Infrastructure (AMI). Modular adaptable/dynamic building envelope (ADBE) solutions have been proven an effective solution—exploiting free façade areas instead of roof areas—for extending the thermal inertia and energy harvesting capacity in existing buildings of different nature (residential, commercial, industrial, etc.). This study presents the PLUG-N-HARVEST holistic workflow towards the delivery of an automatically controllable microgrid integrating active ADBE technologies (e.g., PVs, HVACs). The digital platform comprises cloud AI services and functionalities for energy-efficient management, data healing/cleansing, flexibility forecasting, and the security-by-design IoT to efficiently optimize the overall performance in near-zero energy buildings and microgrids. The current study presents the effective design and necessary digital integration steps towards the PLUG-N-HARVEST ICT platform alongside real-life verification test results, validating the performance of the platform.

Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns.