Institutional buildings still lack effective, real-time mechanisms to detect energy consumption anomalies, causing inefficiencies, higher costs and environmental impact. Many current solutions remain centralized or isolated, which limits real-time detection, Internet of Things (IoT) integration and distributed intelligence. This gap is addressed by the Electricity Consumption Anomaly Detector (eCAD), a system that combines multi-agent architectures, artificial intelligence, and IoT within an edge-computing infrastructure that enables autonomous, real-time anomaly detection with minimal latency and efficient use of resources. eCAD processes heterogeneous data sources, including Wi-Fi access-point data, temporal patterns, and occupant-related variables such as academic context. This comprehensive feature set enables context-aware, autonomous identification of anomalous consumption. The system was deployed on the Smart University platform at the University of Alicante, where distributed agents collect, process, and visualize energy data in real time. Experimental results show an accuracy of 91.02%, with low false-positive and false-negative rates. Unlike most prior approaches, the proposed architecture offers a distinctive contribution: a holistic and scalable framework that integrates data acquisition, analysis, and visualization at the edge, validated in a real-world academic environment. Complementary analyses further confirmed the stability, operational impact, and scalability of the system.

Fog computing can provide an effective solution to the challenges presented by today’s ever-emerging Internet of Things (IoT) infrastructures. As the number of interconnected devices progressively increases, these infrastructures require better solutions to ensure high scalability and processing capacity, along with an efficient use of available resources. This is why this paper presents a distributed Fog architecture, specifically designed to address the challenges and difficulties presented by heterogeneous IoT environments. This Fog architecture is used as an intermediate layer between the IoT devices and the final layer, it has been designed after the previous analysis of the requirements to be met for the solution, then the modularization of the architecture has been carried out so that it can be easily distributed, and finally, an implementation has been generated on a real environment as a validation case of the proposal.

This study introduces Fort2BCK, an advanced security framework designed to mitigate critical vulnerabilities in healthcare blockchain implementation, specifically data manipulation, unauthorised access and weaknesses in consensus protocols. Fort2BCK employs a dual verification mechanism, combining native consensus algorithm validation with the application of advanced cryptographic signatures (RSA, ECDSA and zero knowledge proofs, ZKPs), thus providing an additional layer of authentication, auditing and resistance to malicious attacks. In contrast to traditional approaches, Fort2BCK significantly reduces the risks of fraud and forgery by independently cryptographically verifying each block before it is integrated into the blockchain, strengthening security in scenarios where conventional consensus models may be vulnerable. In addition, its interoperability with multiple blockchain architectures, including proof of work (PoW), proof of stake (PoS) and delegated proof of stake (DPoS), allows it to effectively mitigate attacks such as the 51% attack in PoW and the nothing-at-stake problem in PoS, through an integrated external validation layer. To evaluate the effectiveness of Fort2BCK, experiments were conducted on a simulated hybrid blockchain network with 100 nodes and 50,000 transactions. The results revealed that Fort2BCK increases security by 35% against block rewrite attacks and decreases the rate of fraudulent transactions by 42%, compared to conventional blockchain systems, while maintaining a computational overhead of less than 8%. Additionally, Fort2BCK ensures compliance with regulations such as HIPAA and GDPR, ensuring that blockchain systems for the healthcare sector meet legal and privacy requirements. These findings demonstrate that Fort2BCK optimises the security, scalability and privacy of medical blockchains, facilitating the secure digitisation of healthcare systems and strengthening trust in clinical data management.

Traditionally, drinking water supply infrastructures have been designed to store as much water as possible and to do so during the energy cheap hours. This approach is unsustainable today. The use of digital systems capable of modeling the behavior of infrastructures and the creation of intelligent control systems can help to make drinking water supply systems more efficient and effective, while still meeting minimum service requirements. This work proposes the development of a control system, based on multi-agent systems (MAS), capable of generating an intelligent control over a drinking water infrastructure, based on the use of local interests of the agents and with an emergent behavior coherent with the needs. To validate the proposal, a simulator based on the infrastructures of a medium-sized Spanish city of 5000 inhabitants has been built and the control has been simulated using the MAS. The results show how the system can maintain the objectives set, handling unknown situations, and facilitating the development of future physical systems based on a just-in-time paradigm that guarantees sustainability, as it allows the generation of virtualizations of the infrastructures and their behavior, thus being able to study the best option for an infrastructure to resolve a supply situation.