This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).

The widespread adoption of the Internet of Things (IoT) has led to a notable rise in the number of interconnected smart devices. Within this IoT environment, we perceive an immense diversity of IoT devices originating from numerous manufacturing entities. They differ in size, form, storage, and processing capability, as well as in their set of features, capabilities, and limitations. In this heterogeneous environment, one of the most arduous tasks is to guarantee security and privacy, especially for resource-constrained devices that possess limited power and low processing capability. Due to these limitations, these devices often lack robust security functions, rendering them prone to malevolent actors or unauthorized users who can access private information or alter device functions. In this context, one of the most critical security challenges in this domain is to accurately identify each device in an IoT network. The efficient management of the IoT ecosystem hinges on the ability to uniquely identify each connected device. This, in turn, simplifies the process of monitoring, updating, and troubleshooting these devices for administrators, contributing to the establishment of a trustworthy and secure IoT network. To cope with this challenge, surveying the existing approaches within the IoT identification literature is of paramount importance to provide the IoT research community with relevant insights into the latest achievements. Furthermore, we aim to provide a thorough and comprehensive study of the key literature related to IoT identification research by elaborating proposals that rely on various techniques covering the physical, network, and application layers of the IoT ecosystem. We conclude this survey by discussing the persistent challenges and unresolved issues that may impede the mainstream adoption of IoT, prohibiting several entities from fully leveraging the potential of this evolving technology.

The rapid increase in the number of Internet of Things (IoT) has brought numerous benefits and conveniences, but it has also introduced significant cybersecurity challenges. IoT devices are often connected to networks and to the internet, making them potential targets for cyberattacks (e.g., Mirai attack). As the IoT ecosystem grows, there’s a pressing need to enhance cybersecurity measures to ensure the protection of these interconnected devices and the data they handle. On the other hand, Cyber Threat Intelligence (CTI) involves the collection, analysis, and distribution of information about potential and ongoing cyber threats. CTI helps organizations understand the threat landscape and take proactive measures to mitigate risks. In the context of IoT, having timely and accurate CTI is crucial for identifying emerging threats and vulnerabilities. While sharing CTI knowledge is important, doing so in a distributed IoT environment presents challenges. The data generated and collected by IoT devices can be sensitive and private, making it essential to find ways to share CTI knowledge without compromising data privacy. This research focuses on leveraging Federated Learning (FL) techniques to facilitate secure and privacy-preserving CTI knowledge sharing in IoT networks by proposing a solution called FedCTI. This paper explores the potential benefits, architecture, experiments, and challenges associated with using FL for sharing CTI knowledge to secure IoT systems.