Despite being a cornerstone of scientific progress, reproducibility remains a key challenge in deep reinforcement learning (DRL). This paper presents a reproducibility study of the RELIANCE method, a DRL-based approach for drone collision avoidance originally proposed by Ouahouah et al. We identify critical flaws in RELIANCE’s exploration strategy and reward function, which impede learning efficiency and task completion. By refining the exploration strategy and introducing a distance-based reward shaping mechanism, we achieve notable improvements in collision avoidance performance, reward accumulation, and success rates. Our modifications yield a 17-fold increase in success rates and a 15.56% reduction in collisions, alongside higher cumulative rewards. These findings highlight the vital role of early exploration and carefully crafted reward functions in DRL applications.
To address rising Quality of Service (QoS) requirements in beyond 5G systems, this paper proposes a data-centric approach for user plane QoS control in beyond 5G systems. Building upon the PSBA-XG platform, previously designed for control plane optimization, we extend its capabilities to monitor and manage the User Plane Function (UPF) performance. By aggregating standard-compliant usage reports using a publish/subscribe architecture, our solution enables rapid detection of QoS degradation and reconfiguration of user sessions. Experimental results on an open-source 5G testbed demonstrate that our approach allows for more flexible QoS management, notably rerouting user traffic to edge resources in case of congestion, than a plain 5G system, without introducing additional complexity. These results highlight the potential of data-centric architectures to enhance QoS in future mobile network systems.
Ransomware has evolved into a full-blown, multi-stage, persistent threat attacking the very core of our critical infrastructures, from cloud to IoT. Although many strategies have been proposed for defense, there is a lack of a comprehensive view on how they relate to the development process and integrate recent advancements. In this paper, we provide an organized overview of ransomware defense by thoroughly examining peer-reviewed articles published between 2018 and 2025 from reputable venues (e.g., IEEE, ACM). We propose a comprehensive taxonomy for detection techniques, encompassing static, behavioral, ML-based, and pre-encryption methods. Our analysis compares real-life deployment challenges such as dataset scarcity and model generalizability against emerging trends such as fileless and browser-based attacks. By identifying significant research gaps in adversarial robustness and explain ability, this survey outlines an actionable agenda to develop scalable, privacy-preserving defenses against ransomware as next-generation defenses continue to evolve.
Vehicular visible light communication (V VLC) systems, when combined with reconfigurable intelligent surfaces (RIS), present promising opportunities for improving communication reliability and efficiency in vehicle to vehicle (V2V) environments. Nevertheless, safeguarding these systems at the physical layer remains a critical challenge, particularly given their exposure to jamming threats. In this study, we investigate the physical layer security performance of RIS assisted V2V VLC systems under jamming scenarios, employing realistic V2V VLC channel models. We develop a methodology to examine the impact of various security strategies in mitigating the adverse effects of jamming. Our analysis examines key parameters including the signal to noise ratio SNR, the secure communication rate and the count of RIS units. Simulation results confirm that the proposed security systems significantly enhance the resilience of V2V VLC networks in the presence of jamming attacks. These results offer useful perspectives for the reliable design structure and deployment of RIS based V2V VLC systems in practical vehicular communication settings.
Despite years of research and the introduction of advanced features in 5G systems, no commercially available 5G device has yet met the stringent latency and reliability requirements defined for ultra-reliable and low-latency communication (URLLC). This raises fundamental questions about whether and how URLLC can be practically implemented under current technological constraints. To answer this question, in this work, we explore the combination of several features defined by the 3GPP standardization, including link adaptation, retransmission schemes, and scheduling algorithms. We evaluate the performance of these techniques to investigate the extent to which the requirements of URLLC can be achieved by using the NS-3 5G LENA simulator. Our results demonstrate that the shortest latency at which a reliability level of 99.999% can be achieved is 4 milliseconds, provided that the wireless channel is of exceptionally high quality. This level of performance is enabled by combining a retransmission scheme with an effective scheduling algorithm. However, in the presence of poor channel quality, it becomes necessary to relax the reliability constraint and/or provide more resources to the transmission to maintain system feasibility.
