Intelligent vehicles have significantly influenced the advancement of Intelligent Transportation Systems (ITS). Smart city consumers increasingly depend on vehicular cloud services, highlighting the need for a stronger Internet of Vehicles (IoV s) architecture. Moreover, smart cities deliver high-performance cloud services using multiple technologies, increasing concerns about communication security across entities exchanging indi-vidual requester data. An intelligent privacy-preserving Intrusion Detection System (IDS) is needed to secure IoV data. This work presents a Federated Learning (FL) approach for intermittent IoVs that uses Deep Swarm Particle Optimisation (DSPO) to choose features optimally while protecting user privacy. This approach enables remote IoVs to access shared data securely, ensuring operational confidentiality and privacy. By integrating DPSO with FL, it enhances data analysis and model training for IoV s, optimizing deep learning models for efficient feature selection in secured distributed environments. This cooperative technique not only protects data privacy but also fosters collaboration among IoV devices. We evaluate the proposed method using two standard datasets, namely CICloV2024 and CICEVSE2024. Despite the intermittent nature of IoVs and imbalanced datasets, our approach gives the highest performance.

Intelligent Environments (IE) are a very active area of research and a number of applications are currently being deployed in domains ranging from smart home to e-health and autonomous vehicles. In a number of cases, IE operate together with (or to support) humans, and it is therefore fundamental that IE are thoroughly verified. In this paper we present how a set of techniques and tools developed for the verification of software code can be employed in the verification of IE described by means of event-condition-action rules. In particular, we reduce the problem of verifying key properties of these rules to satisfiability and termination problems that can be addressed using state-of-the-art SMT solvers and program analysers. We introduce a tool called vIRONy that implements these techniques and we validate our approach against a number of case studies from the literature.

A Wireless Sensor Network (WSN) is composed of distributed sensors with limited processing capabilities and energy restrictions. These unique attributes pose new challenges amongst which prolonging the WSN lifetime is one of the most important. Clustering is an energy efficient routing technique that has been widely applied to report data from the WSN nodes to a centralised Base Station. A plethora of different clustering protocols have been proposed. Some protocols are based on equal-sized clusters while others use clusters of unequal size. Some others make use of rotation techniques to reduce the amount of cluster head elections. When different clustering approaches are presented different simulation settings are used. In this paper we perform a comparison study of HEED based clustering protocols that are HEED, UHEED, RUHEED and a novel variation of R-HEED that is ER-HEED. We have considered the same network model, the same energy consumption model and we have compared the lifetime of the protocols by considering various case studies. Our comparison study shows that the selection of the protocol to be used depends on the case study and the WSN lifetime measure that is considered.

In a service-oriented architecture, systems communicate by exchanging messages. In this work, we propose a formal model based on OCL-constrained UML Class diagrams and a methodology based on Alloy Analyzer respectively for describing and verifying any first-order constrained client-server conversations. This framework allows us to verify conversation protocol designs at a fairly detailed level and to check first-order logic constraints on both message flows and message contents.