The convergence of the Internet of Things (IoT) and the metaverse creates systems where a Digital Twin (DT) sits between physical and virtual worlds. Keeping them in sync is hard when actions arrive at the same time with different priorities and delays. We present a DT template that supports two-way, asynchronous interactions and a stack of three policies: confirm matching intents, resolve conflicts with context (domain weights and timeliness), and rollback on invariant violations. We validate the approach in a smart building with DTs representing doors, windows, and lights operating within policy domains that include security, comfort, and energy efficiency. Without policies, conflicting windows already reach 33-56% with only two users for different request rates. With the policy stack, the share of conflicts resolved by policy increases with the reliability gap and can approach all the conflicts, reducing rollbacks. Finally, under asymmetric placement, by simulating the DT at different points in the network (e.g., edge close to physical devices and cloud close to the metaverse), rollbacks shift to the slower side but leave the overall resolution essentially unchanged.

Digital Twins (DTs) are increasingly adopted in IoT systems as autonomous agents capable of providing services and interacting within distributed ecosystems. However, most trust management solutions rely on overly idealized assumptions: service evaluations are always accurate, and benevolent nodes never make mistakes. These assumptions limit the applicability of such models in realistic scenarios, where uncertainty and imperfection are the norm. This paper proposes a trust management model tailored to more realistic DTs, where both service providers and requesters may introduce errors. Providers may fail due to physical or software limitations; requesters cannot rely on objective ground truth to evaluate outcomes. To capture this complexity, we introduce a feedback mechanism based on divergence from aggregated results, rather than binary correctness. We define an IoT scenario where DTs dynamically offer and request services. A trust computation model is applied to distinguish truly malicious nodes from those affected by faults, reducing false positives. Simulations confirm improved trust accuracy and system resilience under realistic conditions.

The Internet of Things (IoT) is increasingly supporting the tourism sector by enabling adaptive services that enhance the visitor experience. Among emerging applications, itinerary planning has gained significant attention, leveraging IoT data to deliver personalized and adaptive routes. However, current systems show key limitations. Personalization often depends on static forms rather than adaptive learning; validation is usually restricted to simulations, and the few real implementations mostly rely on mobile apps, tools that tourists are reluctant to download and often abandon after limited use. To overcome these challenges, this paper proposes an itinerary planning system that integrates reinforcement learning for tourist profiling with a genetic algorithm for multi-objective optimization, implemented in a real-world scenario through a cloud infrastructure and delivered via an interactive totem that serves as the access point for tourists. Results confirm both the efficiency and scalability of the approach, showing that the system can be seamlessly extended to diverse urban contexts.

The rapid proliferation of connected devices in Internet of Things (IoT) ecosystems has created significant challenges for efficient service discovery, with traditional approaches suffering from scalability limitations and excessive resource consumption. This paper presents a novel utility-driven service discovery model for Social Digital Twins (SDTs) environments that optimises resource utilization while maintaining discovery effectiveness. Our proposed model introduces a dynamic utility function that evaluates the contribution of each social relationship among digital twins to the discovery process, enabling nodes to make intelligent forwarding decisions with only local knowledge. By balancing exploitation of high-utility paths with exploration of potentially valuable alternatives, the system adapts to network conditions and service distribution patterns. Extensive simulations on a large-scale SDT dataset demonstrate that our service-specific utility approach achieves 41.7% efficiency, outperforming flooding-based techniques (2.54% efficiency) by a factor of 16.4 while maintaining high service discovery rates. The model shows particular effectiveness in dynamic environments where service availability fluctuates, making it suitable for next-generation IoT deployments where scalability and resource efficiency are critical concerns.

The growth of the Internet of Things (IoT), characterized by billions of interconnected devices represented by Digital Twins (DTs), poses significant challenges in ensuring reliable communication. While Service Level Agreements (SLAs) and Key Performance Indicators (KPIs) offer a foundation for performance monitoring, they are insufficient in decentralized scenarios where devices frequently interact without knowing each other. In this context, Trust Management Systems (TMSs) emerge as a possible solution to support cooperation, evaluating the reliability of both data and DTs. In this context, this paper addresses the problem of trust in the IoT by modeling interactions among DTs through a game-theory approach, where each DT is seen as a game-rational player. Based on this model, we derive a set of design guidelines for the development of TMSs that consider both errors and malicious behaviours. Furthermore, we apply these guidelines to assess and compare several recognized TMSs from the literature, highlighting their strengths and limitations.

Digital Twin (DT) technology is expected to cover a crucial role in a variety of 6G application scenarios, including smart automotive, smart home and smart city. By leveraging advanced Artificial Intelligence (AI) modules alongside cutting-edge communication and networking architectures, DTs will be able to develop cognitive and social skills and build relationships with each other, thus facilitating the sharing of services and experience. However, in current implementations, DTs typically engage with their physical counterpart only, for predictive main-tenance and optimization, while protocols for inter-twin commu-nications and service discovery are still unexplored. In this paper, we focus on service discovery and provisioning in DT networks hosted at the network edge. In our design, the Social Internet of Things (SIoT) notion is applied to build social networks among DTs and, in parallel, name-based primitives, according to the Information Centric Networking (ICN) paradigm, are consid-ered to support inter-twin interactions. Two distributed name-based service discovery mechanisms are envisioned: a social-driven scheme, leveraging friendship and similarities among DTs' names, and a network-driven scheme, leveraging the ICN forwarding fabric only. A performance evaluation shows the benefits of the conceived solution in terms of reduced discovery latency compared to legacy centralized approaches.

Due to the continuous expansion of the Internet of Things (IoT) and its related applications, service discovery nowadays represents a crucial mechanism that enables devices to look efficiently for the desired services. In this regard, a new paradigm, namely Social IoT, has been recently introduced according to which the devices are capable of establishing social relationships in an autonomous way with respect to the rules set by their owners. Within this scenario, “things” interact opportunistically with their peers to provide composite services for the benefit of human beings. In this sense, this paper proposes an exhaustive analysis of the main parameters needed to implement service discovery mechanisms for the Social IoT and studies their relative importance based on a dataset of real objects. On the basis of the parameters' importance, then an efficient service discovery algorithm is proposed, and experiment evaluations are conducted to show its performance in comparison to traditional approaches. Final simulations prove that the proposed mechanism can discover desired services in a fast and autonomous manner.

Dynamic allocation of frequency resources to nodes in a wireless communication network is a well-known method adopted to mitigate potential interference, both unintentional and malicious. Various selection approaches have been adopted in literature, to limit the impact of interference and keep a high quality of wireless links. In this paper, we propose a different channel selection method, based on trust policies. The trust management approach proposed in this work relies on the node’s own experience and trust recommendations provided by its neighbourhood. By means of simulation results in Network Simulator NS-3, we demonstrate the effectiveness of the proposed trust method, while the system is under jamming attacks, in respect of a baseline approach. We also consider and evaluate the resilience of our approach in respect of malicious nodes, providing false information regarding the quality of the channel, to induct bad channel selection of the node. Results show how the system is resilient in respect of malicious nodes, keeping around 10% of throughput more than an approach only based on the own proper experience, considering the presence of 40% of malicious nodes, both single and collusive attacks.

Energy usage inside buildings is a critical problem, especially considering high loads such as Heating, Ventilation and Air Conditioning (HVAC) systems: around 50% of the buildings’ energy demand resides in HVAC usage which causes a significant waste of energy resources due to improper uses. Usage awareness and efficient management have the potential to reduce related costs. However, strict saving policies may contrast with users’ comfort. In this sense, this paper proposes a multi-user multi-room smart energy management approach where a trade-off between the energy cost and the users’ thermal comfort is achieved. The proposed user-centric approach takes advantage of the novel paradigm of the Social Internet of Things to leverage a social consciousness and allow automated interactions between objects. Accordingly, the system automatically obtains the thermal profiles of both rooms and users. All these profiles are continuously updated based on the system experience and are then analysed through an optimization model to drive the selection of the most appropriate working times for HVACs. Experimental results in a real environment demonstrated the cognitive behaviour of the system which can adapt to users’ needs and ensure an acceptable comfort level while at the same time reducing energy costs compared to traditional usage.

Technological development combined with the evolution of the Internet has made it possible to reach an increasing number of people over the years and given them the opportunity to access information published on the network. The growth in the number of fake news generated daily, combined with the simplicity with which it is possible to share them, has created such a large phenomenon that it has become immediately uncontrollable. Furthermore, the quality with which malicious content is made is increasingly high so even professional experts, such as journalists, have difficulty recognizing which news is fake and which is real. This paper aims to implement an architecture that provides a service to final users that assures the reliability of news providers and the quality of news based on innovative tools. The proposed models take advantage of several Machine Learning approaches for fake news detection tasks and take into account well-known attacks on trust. Finally, the implemented architecture is tested with a well-known dataset and shows how the proposed models can effectively identify fake news and isolate malicious sources.

The development of network infrastructure and the growth of technology has made it possible to expand the number of users connected to the Internet, enabling them to access billions of news and information. Moreover, the spread of social media has provided an opportunity for everyone to make their opinion heard. However, on the other side, the new technological features have allowed malicious users to exploit their advantage and spread misleading or fraudulent news. Within this scenario, our paper proposes a fake news detection algorithm that classifies information according to several parameters: the writing style, sentiment analysis, and news context. Furthermore, the model takes advantage of Blockchain technology, which provides the outline of digital contents authority proof. Finally, simulations with a well-known dataset show how the proposed model can effectively isolate fake news and distinguish them from real ones.

The integration of social networking concepts into the Internet of Things (IoT) has led to the so called Social Internet of Things paradigm, according to which the objects are capable of establishing social relationships in an autonomous way with respect to their owners. Within this scenario, “things” interact opportunistically with their peers to seek needed services. However, attacks and malfunctions in the IoT can outweigh any of its benefits if not handled adequately. In this article, we focus on the possible types of trust attacks that can affect the IoT and propose a trust management model able to overcome all the analyzed attacks. Simulations show how the proposed model can effectively isolate almost any malicious nodes in the network at the expense of an increase in the number of transactions needed for the model to converge.

Node, service and information discovery as well as trust management are key issues that characterize the IoT when huge numbers of nodes have to collaborate to support the deployed applications. A recent promising proposal, with the ability to address these issues, is the Social IoT (SIoT) paradigm, whose main principle is to enable objects to autonomously establish social links with to each other (adhering to rules set by their owners). To be able to test and validate this ability, significant datasets regarding objects' networks (node description, typology, activities, exchanged traffic) are needed, which however are not completely available. This paper addresses this issue by presenting a dataset that has been realized on the basis of real IoT objects available in the city of Santander and categorized following the typologies and data model for objects introduced in the FIWARE Data Models. Object profiles and guidelines for the relationships' creation for the SIoT are described and the obtained data and the resulting social network is made available to the research community.