The Controller Area Network (CAN) protocol is the main communication backbone in modern vehicles, enabling data exchange between Electronic Control Units (ECUs). However, its lack of built-in security mechanisms exposes Connected Vehicles (CV) to a growing range of cyber threats. To address this, we propose a distributed intrusion detection framework that integrates Model-Agnostic Meta-Learning (MAML) with Federated Learning (FL), offering adaptability and privacy preservation. The system leverages an LSTM-based model to learn temporal patterns in CAN message IDs, enabling the detection of known and previously unseen attacks. MAML enhances the model's generalization by facilitating rapid adaptation to new threat types using limited data, while FL enables collaborative training across multiple CVs without sharing raw data. Our distributed approach ensures continuous learning and robustness in real-world automotive environments. Experimental results demonstrate the framework's effectiveness in detecting complex intrusions while maintaining data privacy across participating vehicles.
This paper addresses the joint design of Unmanned Aerial Vehicles (UAVs) trajectory and radio resource management (RRM) in dynamic wireless environments by leveraging a multi-agent deep reinforcement learning (MADRL) framework. In contrast to prior works that either assume constant synchronization between agents and the controller or overlook the communication cost, we explicitly model the interaction between UAVs and the central controller. We propose an adaptive synchronization strategy that selectively transmits model parameters and experience data based on their relevance, enabling a resource-aware RRM algorithm that optimally balances learning performance and communication overhead. The MADRL agents optimize their trajectories based on rewards that incorporate priorities derived from the RRM layer, which jointly manages both uplink and downlink communications. Simulation results demonstrate that our event-driven synchronization strategy outperforms periodic baselines in both convergence speed and communication overhead, towards scalable deployment in realistic urban environments.
Dense wireless networks, such as electromagnetic nanonetworks, are characterized by high number of resource-constrained nanonodes close to each other. Testing communication protocols for these networks is an important challenge as real-world experimentation is complex and theoretical analysis is often too limited. Therefore, simulations are widely used as an alternative due to its cost-effectiveness, reproducibility, and efficiency. However, their accuracy depends on how they simulate the real conditions of the network. For instance, simulation accuracy depends widely on the packet loss model used. This paper addresses this challenge, by comparing the Unit Disc Graph (UDG) and shadowing packet loss models in the context of flooding protocols. The obtained results show that while both models can ensure similar outcomes in some cases, they diverge significantly in others, particularly when data delivery is not guaranteed across the entire network. Finally, this study shows the importance of selecting appropriate packet loss models in simulations to ensure reliable and generalizable protocol evaluation.
Non-Terrestrial Networks (NTNs), comprising Unmanned Aerial Vehicles (UAVs) and High Altitude Platform Stations (HAPS) equipped with Mobile Edge Computing (MEC), offer promising solutions for network traffic and tasks offloading from ground users. To enhance the reliability and energy efficiency of such systems, Reconfigurable Intelligent Surfaces (RIS) can be deployed to control wireless signal propagation. In this context, we propose in this paper a novel MEC-enabled framework with HAPS and RIS-equipped UAVs (UAV-RISs) to optimize task offloading from ground users. Our objective is to minimize the tasks' average end-to-end (E2E) delay, under constraints of UAV and HAPS power capacity and E2E service delay threshold, through the optimization of task assignment and UAV-RIS phase-shift configuration. Given the NP-hardness of the problem, we decompose it into two subproblems. The first consists of optimizing the RIS phase shifts to minimize the RIS-assisted communication delay. The second tackles the task assignment problem using a Particle Swarm Optimization (PSO)-based approach, considering the RIS phase shifting solution previously developed. Through simulations, we validate the efficacy of our approach, which outperforms other benchmarks in terms of task average E2E delay and task offloading success rate.
DECT 2020 NR is a Radio Interface Technology (RIT) introduced by ETSI for massive Machine Type Communications (mMTC) and Ultra-Reliable Low Latency Communications (URLLC) use cases. Notably, DECT 2020 NR stands as the sole non-cellular 5G technology apt for industrial IoT (IIoT) scenarios. However, achieving the stringent reliability and latency requirements of URLLC in dense industrial settings poses a significant challenge due to various propagation losses and severe shadowing. In this research work, we propose a novel DECT 2020 NR framework incorporating Intelligent Reflecting Surface (IRS) to meet the challenging URLLC constraints. The framework maps the system model in 3D space, including the 3D mapping of the radiation pattern of IRS, and employs the Separating Axis Theorem (SAT) to systematically identify shadowed regions within this 3D system model and mitigate the identified shadowed areas, leveraging the proposed novel MAC procedure for the IRS-assisted DECT 2020 NR network. Additionally, we propose a resource allocation scheme based on the priority given to radio devices ready to offload the data. Comparative analysis demonstrates that our proposed solution achieves significant improvements in received power, throughput, and reduction in round-trip time (RTT) when compared to both the existing approach and the conventional DECT 2020 NR standard.
