The sixth generation of wireless networks envisions intelligent and adaptive environments capable of meeting the demands of emerging applications such as immersive extended reality, advanced healthcare, and the metaverse. However, this vision requires overcoming critical challenges, including the limitations of conventional wireless technologies in mitigating path loss and dynamically adapting to diverse user needs. Among the proposed reconfigurable technologies, pinching antenna systems (PASs) offer a novel way to turn path loss into a programmable parameter by using dielectric waveguides to minimize propagation losses at high frequencies. In this paper, we develop a comprehensive analytical framework that derives closed-form expressions for the outage probability and average rate of PASs while incorporating both free-space path loss and waveguide attenuation under realistic conditions. In addition, we characterize the optimal placement of pinching antennas to maximize performance under waveguide losses. Numerical results show the significant impact of waveguide losses on system performance, especially for longer waveguides, emphasizing the importance of accurate loss modeling. Despite these challenges, PASs consistently outperform conventional systems in terms of reliability and data rate, underscoring their potential to enable high-performance programmable wireless environments. 

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks. 

The evolution toward sixth-generation (6G) wireless networks has introduced programmable wireless environments (PWEs) and reconfigurable intelligent surfaces (RISs) as transformative elements for achieving near-deterministic wireless communications. However, as RIS capabilities advance with the addition of more reflecting elements, the need for high-precision user localization becomes increasingly critical, since inaccurate localization could lead to erroneous RIS configuration. In this direction, this paper investigates the integration of RISs and optical anchors in PWEs, emphasizing the crucial role of ultra-precise localization in unlocking advanced electromagnetic functionalities. Specifically, we present an in-depth analysis of various localization techniques, both RIS-based and RIS-independent, while introducing the concept of empowering PWEs with optical anchors for enhanced localization precision. Our findings highlight that accurate localization is essential to fully exploit the capabilities of RISs, paving the way for future applications. Through this exploration, we contribute to the advancement of PWEs in line with 6G networks and the improvement of service quality in next-generation wireless networks. 

The advent of Programmable Wireless Environments (PWEs) has transformed the wireless propagation phenomenon into a software-defined resource, leveraging Software defined metasurfaces (SDMs). These new technologies have shown that wireless waves can be routed within a space, contrary to the regular, chaotic wireless propagation, yielding considerable benefits to communication efficiency, and even completely new applications. A new topic has risen, in the context of modeling these capabilities as network resources, and allocating them to users. In this context, the present paper contributes HERA, a heuristic resource allocator that receives a setup with users and SDMs as input, and produces the necessary configuration of the latter, for efficient shared performance. Simulation results demonstrate considerable efficiency in this task, while the heuristic nature of HERA sets the basis for a flexible definition of complex user objectives in the future.

In an era of escalating cyber threats, the imperative for robust and comprehensive cybersecurity measures has never been more pressing. To address this challenge, SYNAPSE presents a pioneering approach by conceptualising, designing, and delivering an Integrated cybersecurity Risk & Resilience Management Platform. The innovation of this platform lies in the integration of key elements, such as situational awareness, incident response, and preparedness (i.e., cyber range), augmented by advanced AI capabilities. Through its holistic approach, SYNAPSE aims to elevate cyber resilience by not only mitigating threats but also fostering a culture of proactive defence, informed decision-making, and collaborative response within organisations and across industries. 

The use of IoT systems in industrial environments provides tremendous benefits and economic value leading to an exponential rise in their adoption. Their extended use, however, does not come without concerns related to potential security threats, thereby creating an obstacle in their further use in the field. To address these security concerns, we introduce a specialized Industrial Intrusion Detection System (I2DS). Our proposed system merges the capabilities of deep learning (DL) with FPGA-based hardware acceleration techniques, enabling it to detect subtle anomalies and potential cyber threats that may evade conventional rule-based intrusion detection systems (IDS) in an effective way. More specifically, by implementing the system on FPGA hardware, we achieve low-latency, high-throughput processing of network traffic, essential for real-time intrusion detection in industrial settings. Our architecture is scalable and can be adapted according to network bandwidth requirements, while remaining lightweight, making it an ideal solution for the stringent resource constraints often encountered in IoT environments. The proposed solution has been validated with the modbus TON-IoT dataset, achieving up to two orders of magnitude higher performance compared to a software equivalent implementation.