Digital Twin Networks (DTNs) are an emerging paradigm where DTs collaborate to share knowledge and deliver intelligent services. To meet the need for low-latency and localized processing, DTs can be deployed at the network edge. However, effective mechanisms for inter-twin communication and service provisioning remain largely unexplored. In this paper, we address these challenges by leveraging a name-based communication paradigm, namely Information Centric Networking (ICN), enhanced with semantic-awareness. To overcome the limitations of exact name matching, we integrate deep learning models into the ICN forwarding fabric to compute semantic similarity. This enables the discovery of relevant cached service results or routes to DT service providers, even under naming heterogeneity. A preliminary evaluation shows improved service provisioning success and reduced latency compared to standard ICN delivery.

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.

Recent advances in multimodal sensing and language modeling have enabled the creation of Human Digital Twins (HDTs) capable of representing users across physiological, behavioral, and semantic dimensions. However, existing approaches often focus on isolated data streams or application-specific knowledge, limiting their ability to support proactive, context-aware identification and fulfillment of user needs. This paper presents a novel HDT framework that integrates structured sensor data, unstructured linguistic inputs, and application-level events into a unified, ontology-driven semantic space. We introduce an aggregated ontology to model user-related entities and relationships, enabling the construction of a Personal Knowledge Graph (PKG) tailored to individual profiles. The PKG serves as a dynamic knowledge representation layer, supporting temporal alignment, semantic reasoning, and low-complexity querying for user need identification. We validate our framework through simulations based on multimodal datasets, showing that contextual integration significantly enhances the system’s ability to identify health-related needs across diverse scenarios.

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.

Climate change mitigation requires the urgent reduction of Greenhouse Gas (GHG) emissions, with the building sector as a significant contributor. This study develops a system to identify appliance profiles from smart meter data, enhancing energy consumption awareness and management. These profiles provide valuable insights into users’ consumption patterns and habits, enabling more accurate load consumption prediction and effective appliance scheduling strategies. The proposed approach employs feature extraction techniques to characterise energy consumption profiles, followed by k-means clustering to identify distinct appliance profiles. Eleven representative features are identified, offering comprehensive insights into occupants’ energy usage habits. The evaluation with real-case data shows accurate consumption cycle approximations for each profile, with errors consistently below 10%. Performance assessment using classification metrics indicates well-characterised and representative profiles, outperforming state-of-the-art methods with average values exceeding 0.88 for all considered metrics. This system helps raise occupants’ awareness of appliance energy usage and facilitates optimised scheduling through an Energy Management System (EMS). By promoting more efficient energy consumption, the proposed approach contributes to overall energy reduction and, consequently, lower GHG emissions in the building sector. • Development of a system to monitor consumption habits for different appliances. • Design a methodology to identify key features for diverse appliance profiles. • Identification of 11 robust features representing consumption profiles effectively. • Integration of k-means clustering to group appliances by consumption and behaviour. • Validation of the proposed system using a real-case dataset.

Today, we are witnessing an ever increasing demand for ubiquitous user connectivity, and at the same time, the surging of advanced handheld devices, with enhanced storage and computing capabilities. These advanced devices can not only satisfy the needs of their owners but also act as a host to provide related services to users nearby. In this regard, the concept of IoT federated infrastructure, where heterogeneous devices pool their resources to match wideranging user requirements, has been attracting increasing attention. To address the heterogeneity of these devices and achieve efficient system resource utilization, in this paper, we introduce a novel IoT Federated Infrastructure-as-a-Service(FDIaaS) framework to support the opportunistic cooperation among IoT devices. In this context, IoT devices are transformed to a Micro-Provider (MP) as a host who can offer their spare resources to the neighboring users. The interaction between the Edge Provider (EP) as a trusted third agent and the MP is modeled as a two-stage Stackelberg game interleaved with a dynamic coalition formation game. Particularly, an incentive mechanism to form FDIaaS using the IoT devices is designed as a centralized optimization problem. Next, to find this incentive policy in a distributed manner, a coalition formation-based solution is proposed. Simulation results show that On average, the successfully executed task rate under the proposed mechanism is only 7% less than the optimal solution. Moreover, when the number of IoT devices increases more federation with higher size formed under our proposed incentive mechanism.

Smart cities use internet to make the day-to-day living of city inhabitants more comfortable and secure. In this regard, long-range wide area networks (LoRaWAN) are ideally suited to provide IoT wireless access networks in smart city applications. LoRaWAN uses the long-range (LoRa) technology at the physical layer to provide long-range wireless connectivity. Considering the shadow fading of the large and closely located buildings in the cities, one of the key challenges in the LoRaWAN deployment is finding the optimal location of gateways while guaranteeing network coverage. In particular, the LoRa gateway deployment becomes more complicated due to the stochastic shadowing of the city buildings due to the random locations of IoT devices. In this paper, a novel analytical framework is proposed that enables the joint gateway placement and spread factor (SF) assignment in LoRaWAN while being cognizant of random building shadow fading. Inspired by the chance-constrained method, a joint stochastic gateway placement and SF assignment problem subject to network coverage constraint is formulated for LoRaWANs. Then, a new unsupervised learning-aware greedy algorithm based on the ““size-constrained weighted set cover” concept is proposed to find an approximate solution to the formulated LoRa gateway planning problem in the city environments. The proposed algorithm is simulated over the campus of Shahid Chamran University of Ahvaz at IRAN. Simulation results demonstrate the effectiveness of the proposed approach compared to the benchmarks. For example, on average, the network coverage probability under our proposed unsupervised learning-aware greedy algorithm is $15 \%$ and $10 \%$ more than the greedy and clustering ones.

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.

The constantly increasing need for data exchange among various types of devices, mobile and fixed, is one of the main characteristics of technological developments in the last few years. Within this context, the possibility to deliver content to more devices into the same domestic environment is very interesting for both consumers and service providers. The main hurdle for device to device (D2D) communications is the available bandwidth and, implicitly, the used radio technology and frequency range. From this point of view, so called TV white spaces (TVWSs) are an ideal candidate for short range communications, but have the problem of interference management with the licensed services already operating there. This problem can be alleviated by using cooperative, distributed spectrum sensing techniques. This paper proposes an innovative approach for D2D communications in a real indoor environment, based on a social Internet of Things (SIoT) architecture able to involve all participating objects in a twofold procedure, gathering both spectrum sensing and quality of service data, and weighting the received information using a novel trustworthiness algorithm. The algorithm, together with the entire SIoT architecture, has been implemented and extensively tested in a real indoor environment.

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.

In the last decade, we have been experiencing an increasing level of intelligence that the objects in the Internet of Things (IoT) have been augmented with, especially in the direction of giving them cognitive and socialization capabilities. We believe that this evolution should go further in the direction of the Emotional Intelligence, which allows humans to be successful in their lives. EmIoT is the resulting paradigm that we propose, which is aimed to increase the Quality of Experience delivered by IoT applications by making IoT capable of: understanding peoples needs by observing them; better management of its own resources on the basis of users emotional state; creating a level of affection to be used for leveraging the level of interaction between IoT and users. The paper's contribution lies in the definition of the paradigm, the analysis of the new functionalities the IoT should be augmented with, and the preliminary investigation of the possible EmIoT architecture.

In this paper the MIFaaS (Mobile-IoT-Federation-as-a-Service) paradigm is proposed to support delay sensitive applications in the Internet of Things (IoT). This objective is reached by leveraging on the federation of distributed services and things at the infrastructure Edge and exploiting the real-world awareness and capabilities of IoT devices at the ground. MIFaaS enables value-added services by implementing the dynamic cooperation among private/public clouds of IoT objects with the purpose to enhance the efficiency in the provisioning of delay-constrained IoT services and increase the number of successfully delivered IoT services. The proposed paradigm is studied in a cellular environment based on standard Long Term Evolution (LTE). The simulative results we present demonstrate how the proposed federation solution of private/public IoT clouds outperforms alternative solutions with no federations and support of resources offered by the Cloud. Moreover, an analysis of the limitations and of the possible enhancements for cellular systems to support the proposed paradigm is drawn.

Fifth generation (5G) systems are expected to introduce a revolution in the ICT domain with innovative networking features, such as device-to-device (D2D) communications. Accordingly, in-proximity devices directly communicate with each other, thus avoiding routing the data across the network infrastructure. This innovative technology is deemed to be also of high relevance to support effective heterogeneous objects interconnection within future IoT ecosystems. However, several open challenges shall be solved to achieve a seamless and reliable deployment of proximity-based communications. In this paper, we give a contribution to trust and security enhancements for opportunistic hop-by-hop forwarding schemes that rely on cellular D2D communications. To tackle the presence of malicious nodes in the network, reliability and reputation notions are introduced to model the level of trust among involved devices. To this aim, social-awareness of devices is accounted for, to better support D2D-based multihop content uploading. Our simulative results in small-scale IoT environments, demonstrate that data loss due to malicious nodes can be drastically reduced and gains in uploading time be reached with the proposed solution.

The huge number of nodes that is expected to join the Internet of Things in the short term will add major scalability issues to several procedures. A recent promising approach to these issues is based on social networking solutions to allow objects to autonomously establish social relationships. Every object in the resulting Social IoT (SIoT) exchanges data with its friend objects in a distributed manner to avoid the need for centralized solutions to implement major functionalities, such as: node discovery, information search and trustworthiness management. However, the number and types of established friendship affects network navigability. This paper addresses this issue proposing an efficient, distributed and dynamic strategy for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection model relies on a Shapley-value based algorithm mapping the friendship selection process in the SIoT onto the coalition formation problem in a corresponding cooperative game. The obtained results show that the proposed solution is able to ensure global navigability, measured in terms of average path length among two nodes in the network, by means of a distributed and wise selection of the number of friend objects a node has to handle.

The Internet of Things (IoT) is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications, and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things (SIoT) has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner, with only local information. In the SIoT it is very important to set appropriate rules in the objects to select the right friends as these impact the performance of services developed on top of this social network. In this work, we addressed this issue by analyzing possible strategies for the benefit of overall network navigability. We first propose five heuristics, which are based on local network properties and that are expected to have an impact on the overall network structure. We then perform extensive experiments, which are intended to analyze the performance in terms of giant components, average degree of connections, local clustering, and average path length. Unexpectedly, we discovered that minimizing the local clustering in the network allowed for achieving the best results in terms of average path length. We have conducted further analysis to understand the potential causes, which have been found to be linked to the number of hubs in the network.

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.

The integration of social networking concepts into the Internet of Things (IoT) has led to the so called Social Internet of Things (SIoT) paradigm, according to which the objects are capable of establishing social relationships in an autonomous way with respect to their owners. The benefits are those of improving scalability in information/service discovery when the SIoT is made of huge numbers of heterogeneous nodes, similarly to what happens with social networks among humans. In this paper we focus on the problem of understanding how the information provided by the other members of the SIoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define a subjective model for the management of trustworthiness which builds upon the solutions proposed for P2P networks. Each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the common friends with the potential service providers. We employ a feedback system and we combine the credibility and centrality of the nodes to evaluate the trust level. Preliminary simulations show the benefits of the proposed model towards the isolation of almost any malicious node in the network.