Recent advances in distributed vehicle-to-vehicle communication promise to transform the user’s driving ex- perience, providing new services capable of improving safety, efficiency and quality of travelling. Due to the large amount of information exchanged, a major challenge of Vehicular Networks is the adoption of appropri- ate data dissemination protocols that ensure good performance in real-time event detection, while guarantee- ing low communication overhead. To this aim, this paper proposes an adaptive event dissemination algorithm which exploits Population Protocols (PPs) for modelling vehicle interactions as coordinated behaviors of au- tonomous agents in a distributed system. The experimental evaluation performed on realistic vehicle tracks over real-world maps demonstrates the system’s ability to efficiently disseminate information in the network in order to support reliable and distributed event detection services.

The rapid convergence of IoT technologies and artificial intelligence is reshaping university campuses into dynamic, smart environments. Faced with the challenge of managing increasingly complex and heterogeneous data streams, campus communities often struggle to benefit fully from available digital resources and personalized support. In response, this work presents a modular and scalable system designed to provide context-aware recommendations that enhance both academic and social experiences. By integrating data from physical sensors, mobile devices, and external sources, the proposed framework captures rich contextual insights to deliver adaptive, personalized services that address the diverse needs of students, faculty, and administrative staff. Developed as part of the S3 Campus project at the University of Palermo, this system represents a significant step forward in fostering innovative, intelligent campus solutions that are attuned to the evolving demands of modern educational environments.

Recommender systems have become pivotal in modern digital platforms, guiding user choices and driving engagement. However, their widespread adoption has also made them a prime target for adversarial attacks, especially data poisoning attacks that subtly manipulate recommendations. Existing approaches often generate unrealistic fake profiles, making them vulnerable to detection by anomaly-based defenses. In this paper, we propose a novel, model-agnostic poisoning framework that combines contrastive learning and reinforcement learning with Proximal Policy Optimization (PPO) to craft highly realistic fake profiles derived from cross-domain user data. By interacting exclusively with a surrogate recommender trained on a compatible domain, our framework identifies and fine-tunes influential user profiles to maximize the impact on a black-box target system. Our experimental evaluation on real-world datasets shows that our approach successfully promotes target items across diverse recommendation models with minimal injection effort, outperforming baseline strategies.

The Smart Connected Communities paradigm, which synergistically integrates smart technologies with the surrounding environment, has paved the way for a new generation of applications that provide increasingly intelligent services by leveraging information coming from users, and the IoT. While user collaboration is essential to improve the quality of information (QoI), the interest of providers in data can jeopardize the right to privacy by revealing details that users are not willing to share (e.g., habits, health status). In addition, not all involved users consistently exhibit cooperative behavior, and the presence of attackers often undermines the quality of the collected information. In this paper, we propose a system for aggregating and analyzing user data without ever compromising their privacy, whilst improving QoI. The system uses Privacy Preserving Computation techniques, clustering, and an outlier removal step to improve the quality of information. Utilizing a real-world dataset, we tested our system, demonstrating its resilience in a scenario with potential attackers and its superior performance compared to other state-of-the-art systems.

The dynamic characteristics of Internet of Things (IoT) systems create major challenges for threat detection systems that rely on machine learning models. Over time, shifts in the statistical distribution of data can lead to drastic performance degradation. This phenomenon is known as concept drift. When this problem occurs, traditional static systems require human intervention to manually retrain, leaving the network vulnerable in the meantime. In this paper, we propose an unsupervised system for online detection of anomalous traffic generated by malware-infected IoT devices. The proposed multi-tier system explicitly accounts for concept drift, automatically retraining only when necessary. We thoroughly tested the system by performing an extensive experimental evaluation using the real-world IoT-23 dataset, which includes network traffic generated by IoT devices as well as malicious network traffic generated by devices infected with different types of malware. We also compared our approach with other state-of-the-art work, and the results showed the remarkable performance achieved by the system using key metrics such as F1 score, accuracy, false positive rate and false negative rate.

Many crowdsensing applications today rely on learning algorithms applied to data streams to accurately classify information and events of interest in smart environments. Unfor-tunately, the statistical properties of the input data may change in unexpected ways. As a result, the definition of anomalous and normal data can vary over time and machine learning models may need to be re-trained incrementally. This problem is known as concept drift, and it has often been ignored by anomaly detection systems, resulting in significant performance degradation. In addition, the statistical distribution of past data often tends to repeat itself, and thus old learning models could be reused, avoiding costly retraining phases on new data, which would waste computational and energy resources. In this paper, we propose a hybrid anomaly detection system for streaming data in smart environments that accounts for concept drift and minimize the number of machine learning models that need to be retrained when shifts in incoming data distribution are detected. The system is multi-tier and relies on two different concept drift detection modules and an ensemble of anomaly detection models. An extensive experimental evaluation has been carried out, using two real datasets and a synthetic one; results show the high performance achieved by the system using common metrics such as F1-score and accuracy.

Assisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularity-, knowledge-, and content-based, according to different hybridization strategies. Experimental evaluation shows the versatility of the hybrid recommender with respect to the other paradigms adopted individually.

Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a Mixed Integer Linear Programming (MILP) problem, and show that the problem is NPHard. Since the solution of such problem requires the knowledge of the user behavioral model, we propose an heuristic based on reinforcement learning with bounded regret to learn such model while optimizing the system performance. Comparison with the state-of-the-art approaches using realistic simulations based on real traces shows that our method outperforms existing schemes in several efficiency metrics. Besides, the results reveal that increasing the amount of produced energy improves the learning ability of the system even in a short period. It gives practical insights for implementation of energy sharing systems.

Multi-agent distributed systems are characterized by autonomous entities that interact with each other to provide, and/or request, different kinds of services. In several contexts, especially when a reward is offered according to the quality of service, individual agents (or coordinated groups) may act in a selfish way. To prevent such behaviours, distributed Reputation Management Systems (RMSs) provide every agent with the capability of computing the reputation of the others according to direct past interactions, as well as indirect opinions reported by their neighbourhood. This last point introduces a weakness on gossiped information that makes RMSs vulnerable to malicious agents’ intent on disseminating false reputation values. Given the variety of application scenarios in which RMSs can be adopted, as well as the multitude of behaviours that agents can implement, designers need RMS evaluation tools that allow them to predict the robustness of the system to security attacks, before its actual deployment. To this aim, we present a simulation software for the vulnerability evaluation of RMSs and illustrate three case studies in which this tool was effectively used to model and assess state-of-the-art RMSs.

In this paper we present a gesture recognition framework for providing the visitors of a museum exhibition with a non intrusive interface for the multimedia enjoyment of digital contents. Early experiments were carried out at the Computer History Museum Exhibition of the University of Palermo.

In distributed environments, where unknown entities cooperate to achieve complex goals, intelligent techniques for estimating agents' truthfulness are required. Distributed Reputation Management Systems (RMSs) allow to accomplish this task without the need for a central entity that may represent a bottleneck and a single point of failure. The design of a distributed RMS is a challenging task due to a multitude of factors that could impact on its performances. In order to support the researcher in evaluating the RMS robustness against security attacks since its beginning design phase, in this work we present a distributed simulation environment that allows to model both the agent's behaviors and the logic of the RMS itself. Moreover, in order to compare at simulation time the performance of the designed distributed RMS with a baseline obtained by an ideal RMS, we introduce an omniscient process called truth-holder which owns a global knowledge all involved entities. The effectiveness of our platform was proved by a set of experiments aimed at measuring the vulnerability of a RMS to a common set of security attacks.

Technological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users' lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users' smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones.

In this short poster paper, we present an application for suggesting attractions to be visited by users, based on social signal processing techniques.