Connected Automated Vehicles (CAVs) utilize their onboard sensors to perceive the environment. The perception range and accuracy can be affected by adverse weather or non-line-of-sight conditions. Cooperative perception or sensor sharing can overcome these limitations by enabling CAVs to exchange sensor data, thus collectively enhancing their perception capabilities. Previous studies have shown the potential of cooperative perception, but limited attention has been given to the fusion of V2X data received through cooperative perception messages with onboard sensor information. The fusion process can be influenced by the quantity and quality of the V2X data. An increased volume of V2X data can reduce uncertainty in the perceived environment; however, when the data is noisy, it may compromise the accuracy of the fusion results. This study investigates the fusion of onboard sensor and V2X data in cooperative perception, and demonstrates that while perception can significantly improve as the V2X penetration rate increases, it can introduce a significant number of false positives if V2X data is not highly accurate. False positives result in the detection of ghost objects that do not actually exist. These ghost objects can, in turn, compromise safety and driving efficiency. Our analysis found that false positives or ghost objects can appear even with accurate V2X data. These findings highlight the challenges in cooperative perception and the importance of developing robust data fusion methods to enhance the reliability of cooperative perception. This is particularly relevant in light of ongoing standardization efforts, such as ETSI TS 103 324 on collective perception.
In the context of the Internet of Things (IoT), an appropriate communication mechanism is crucial to preventing attacks, particularly in IoT applications. The inherent constraints of these devices, such as limited duty cycles, reduced energy capacity, or the inability to listen before transmitting, increase their susceptibility to interference. Low-power wide-area Networks (LPWANs), particularly LoRa, are increasingly used in IoT due to their long-range and low-power capabilities. However, their decentralized structure and the well-known limitations of IoT devices make them vulnerable to attacks, especially jamming. To address this, we propose a ‘mitigation mechanism’ to optimize network performance in LoRa communications. We evaluated the mechanism’s resilience against reactive jamming attacks in simulated environments, showing a clear improvement in network performance over conventional LoRa schemes.
The design of positioning solutions for the Industrial Internet of Things (IIoT) faces several open challenges, particularly in complex indoor environments typical of industrial settings: first and foremost, the limited availability of experimental datasets from realistic scenarios, in particular in relation to private 5G networks.This paper investigates range-free positioning for IIoT in indoor environments using a combination of public and private 5G networks operating at both midband (3.7 GHz) and mmWave (26 GHz) frequencies. The study introduces one of the first publicly available datasets collected in a realistic industrial setting, utilizing synchronization signals from 5G networks. A Weighted k-Nearest Neighbors (WkNN) algorithm is used to perform fingerprinting-based positioning using the experimental data, comparing the achievable accuracy in public vs. private deployments. The results show that private mmWave deployments significantly outperform public networks in terms of positioning accuracy, achieving errors as low as 2.16 meters. Additionally, the study highlights the discriminative power of time-based features such as Time of Arrival (ToA) in enhancing range-free positioning accuracy.
The Service-Based Architecture (SBA) has been successfully employed in the 5G Core since Release 15. Its modular and loosely coupled approach to network function interaction, based on the producer/consumer paradigm, perfectly pairs with the softwarization trend mobile networks have undergone over the last decade. Still, the Radio Access Network (RAN) did not leverage this approach, retaining the traditional Protocol-Based Architecture (PBA). In this paper, we make the case for adopting the SBA in the RAN, a transition we argue is both advantageous and feasible. We first present a comprehensive analysis of the benefits, such as enhanced flexibility and innovation, while also considering potential implementation challenges. Then, we detail how the SBA can be used to provide two exemplary procedures currently performed using the PBA, showing the feasibility of a fully service-based 5G network architecture.
The expansion of 5G networks has led to remarkable data volume and complexity, introducing significant security challenges that require the implementation of robust and scalable anomaly detection mechanisms. Traditional centralized approaches pose privacy risks and scalability challenges due to the distributed nature of 5G infrastructures. Federated Learning (FL) offers a decentralized solution but often overlooks the importance of feature relevance and privacy preservation during model aggregation. This paper introduces a novel Feature-Aware Federated framework that integrates feature importance into the aggregation process while ensuring differential privacy. We employ integrated gradients to compute feature importance for each client, aggregate them globally with differential privacy noise, and use these insights to weight model parameters during aggregation. Additionally, we propose Dynamic Feature Importance Adaptation (DFIA) to update feature importance occasionally, enhancing the model's adaptability to evolving data distributions. Experimental results demonstrate that our framework outperforms traditional federated approaches like FedAvg and FedProx in unsupervised anomaly detection tasks within 5G networks, achieving higher accuracy and robustness while preserving data privacy.
