Angelos Antonopoulos

Sunder Ali Khowaja, Parus Khuwaja, Kapal Dev et al.

The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G technologies such as ultra-dense network topologies, low latency, and high data rates. Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning. Although federated learning has solved data privacy preservation issues to some extent, the technique is quite vulnerable to model inversion and model poisoning attacks. We assume that the design of defense mechanism and attacks are two sides of the same coin. Designing a method to reduce vulnerability requires the attack to be effective and challenging with real-world implications. In this work, we propose simulated poisoning and inversion network (SPIN) that leverages the optimization approach for reconstructing data from a differential model trained by a vehicular node and intercepted when transmitted to roadside unit (RSU). We then train a generative adversarial network (GAN) to improve the generation of data with each passing round and global update from the RSU, accordingly. Evaluation results show the qualitative and quantitative effectiveness of the proposed approach. The attack initiated by SPIN can reduce up to 22% accuracy on publicly available datasets while just using a single attacker. We assume that revealing the simulation of such attacks would help us find its defense mechanism in an effective manner.

Chiara Perillo, Angelos Antonopoulos, Christos Verikoukis

The latest technological advancements in the telecommunications domain (e.g., widespread adoption of mobile devices, introduction of 5G wireless communications, etc.) have brought new stakeholders into the spotlight. More specifically, Over-the-Top (OTT) providers have recently appeared, offering their services over the existing deployed telecommunication networks. The entry of the new players has changed the dynamics in the domain, as it creates conflicting situations with the Internet Service Providers (ISPs), who traditionally dominate the area, motivating the necessity for novel analytical studies for this relationship. However, despite the importance of accessing real observational data, there is no database with the aggregate information that can serve as a solid base for this research. To that end, this document provides a detailed summary report for financial and statistic data for the period 2008-2013 that can be exploited for realistic econometric models that will provide useful insights on this topic. The document summarizes data from various sources with regard to the ISP revenues and Capital Expenditures (CAPEX), the OTT revenues, the Internet penetration and the Gross Domestic Product (GDP), taking into account three big OTT providers (i.e., Facebook, Skype, WhatsApp) and ten major ISPs that operate in seven different countries.

Vaishnavi Kasuluru, Luis Blanco, Engin Zeydan et al.

The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications towards the 6G era. Technologies such as Open Radio Access Network (O-RAN) can help to build interoperable solutions for the management of complex systems. Probabilistic forecasting, in contrast to deterministic single-point estimators, can offer a different approach to resource allocation by quantifying the uncertainty of the generated predictions. This paper examines the cloud-native aspects of O-RAN together with the radio App (rApp) deployment options. The integration of probabilistic forecasting techniques as a rApp in O-RAN is also emphasized, along with case studies of real-world applications. Through a comparative analysis of forecasting models using the error metric, we show the advantages of Deep Autoregressive Recurrent network (DeepAR) over other deterministic probabilistic estimators. Furthermore, the simplicity of Simple-Feed-Forward (SFF) leads to a fast runtime but does not capture the temporal dependencies of the input data. Finally, we present some aspects related to the practical applicability of cloud-native O-RAN with probabilistic forecasting.

Charalampos Kalalas, Pavol Mulinka, Guillermo Candela Belmonte et al.

Artificial intelligence (AI) has been increasingly applied to the condition monitoring of vehicular equipment, aiming to enhance maintenance strategies, reduce costs, and improve safety. Leveraging the edge computing paradigm, AI-based condition monitoring systems process vast streams of vehicular data to detect anomalies and optimize operational performance. In this work, we introduce a novel vehicle condition monitoring service that enables real-time diagnostics of a diverse set of anomalies while remaining practical for deployment in real-world edge environments. To address mobility challenges, we propose a closed-loop service orchestration framework where service migration across edge nodes is dynamically triggered by network-related metrics. Our approach has been implemented and tested in a real-world race circuit environment equipped with 5G network capabilities under diverse operational conditions. Experimental results demonstrate the effectiveness of our framework in ensuring low-latency AI inference and adaptive service placement, highlighting its potential for intelligent transportation and mobility applications.