Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into Passive RIS (PRIS) and Active RIS (ARIS) paradigms. PRIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.

This survey provides an in-depth examination of the role of reconfigurable intelligent surfaces (RIS) in enhancing physical layer security (PLS) within unmanned aerial vehicle (UAV)-assisted networks, which are essential for the secure and efficient operation of sixth-generation (6G) wireless communications. The study covers various types of RIS—passive, active, and hybrid—and their applications in both terrestrial and aerial environments to strengthen PLS. Key focus areas include advanced PLS techniques such as optimizing UAV trajectory, beamforming, and RIS phase-shift configurations, all aimed at improving secrecy rates (SRs) while mitigating the risks of eavesdropping and jamming. Moreover, the survey also addresses strategies for enhancing energy-efficient SRs and implementing anti-jamming mechanisms within UAV-assisted networks. Additionally, it explores the integration of RIS-UAV systems with emerging technologies such as non-orthogonal multiple access (NOMA), mobile edge computing (MEC), cognitive radio, and THz networks, demonstrating how security can be enhanced in such networks. Through detailed performance analysis, the paper highlights the transformative potential of RIS-equipped UAVs in overcoming the potential security challenges for future 6G networks. Finally, the survey presents lessons learned and identifies critical future research directions and open challenges, offering insights that will guide the development of robust and secure RIS-assisted UAV systems in next-generation wireless networks.

Efficient decentralized power management is crucial for enhancing the reliability, resilience, responsiveness, and sustainability of secondary power distribution systems, thereby preventing major power outages and providing rapid responses. However, existing secondary power distribution networks are prone to failures, thus compromising their operational trustworthiness and efficiency. This work proposes an intelligent, decentralized control system with distributed processing capabilities. The proposed system is designed to automate fault detection and rectification along with optimized power management at secondary distribution nodes. The system enables rapid fault detection (line-to-line, line-to-ground, and overload) and initiates a fault-based response to isolate the load through controlled relays. Additionally, an intelligent power management system automatically rectifies surge faults (short-lived faults) and reports non-surge faults (persistent faults) to the control center. It continuously updates the status of real-time power parameters to the database using a Global System for Mobile Communications (GSM)-based communication system with a frequency of 60 s per sample for power management. The Proteus-based simulation and a scaled-down model validate the efficiency and supremacy of the proposed system over the existing control system for power distribution nodes. The results demonstrate that our model detects critical faults and initiates the response within 100 and 200 milliseconds, respectively. Surge faults are automatically rectified within 90 s, while non-surge faults are reported to the database after 90 s. This approach significantly reduces downtime, enables energy accountability, and supports sustainable energy management through a decentralized and distributed control system.

The advent of gravitational wave astronomy (GW) has revolutionized the observation of cataclysmic cosmic events, such as black hole mergers and neutron star collisions. The Laser Interferometer Gravitational-Wave Observatory (LIGO) has been at the forefront of these discoveries. However, the immense volume and complexity of gravitational wave data present significant challenges for traditional analysis methods. This paper investigates the growing synergy between artificial intelligence (AI) and GW science, emphasizing how AI enhances signal detection, noise reduction, and data interpretation. It begins with an overview of GW fundamentals and the role of machine learning in increasing detector sensitivity. Notable GW events observed by LIGO are discussed alongside persistent analytical challenges such as data quality, generalization, and computational constraints. A comprehensive performance review of AI techniques, including supervised learning, unsupervised learning, deep learning, and reinforcement learning is presented based on data spanning 2021 to 2024. Evaluation metrics include accuracy, precision, true positive rate (TPR), false positive rate (FPR), and computational efficiency. Findings indicate that deep learning and supervised learning outperform other approaches, particularly in enhancing TPR and minimizing FPR. While unsupervised and reinforcement learning models offer less precision, they demonstrate high efficiency and potential for real-time applications. The study also explores AI’s integration into next-generation detectors and waveform reconstruction techniques. Overall, the integration of AI into GW research significantly improves the reliability and speed of event detection, unlocking new possibilities for exploring the dynamic universe. This paper provides a comprehensive outlook on the transformative role of AI in shaping the future of GW astronomy.

This comprehensive survey examines how Reconfigurable Intelligent Surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within Single-Input Multiple-Output (SIMO), Multiple-Input Single-Output (MISO), and Multiple-Input Multiple-Output (MIMO) systems. It further explores RIS integration in complex network environments, such as Heterogeneous Wireless Networks (HetNets) and Non-Orthogonal Multiple Access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like Terahertz (THz) networks, Vehicular Communication (VC), and Unmanned Aerial Vehicle (UAV) communications, highlighting the synergy between RIS and Artificial Intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues.

Integrated Sensing and Communication (ISAC) is a crucial component of future wireless networks, enabling seamless integration of Communication and Sensing (C\&S) functionalities. However, ensuring security in ISAC systems remains a significant challenge, as both C\&S data are susceptible to adversarial threats. Physical Layer Security (PLS) has emerged as a key framework for mitigating these risks at the transmission level. Reconfigurable Intelligent Surfaces (RIS) further enhance PLS by dynamically shaping the radio environment to improve both secrecy along with C\&S performance. This survey begins with an overview of RIS, PLS, and ISAC fundamentals, establishing a foundation for understanding their integration. The state-of-the-art RIS-assisted PLS approaches in ISAC systems are then categorized into passive RIS and Active RIS (ARIS) paradigms. Passive RIS-based techniques focus on optimizing system throughput, covert communication, and Secrecy Rates (SRs), alongside improving sensing Signal-to-Noise Ratio (SNR) and Weighted Sum Rate (WSR) under various constraints. ARIS-based strategies extend these capabilities by actively optimizing beamforming to enhance secrecy and covert rates while ensuring robust sensing under communication and security constraints. By reviewing both passive and ARIS-based security frameworks, this survey highlights the transformative role of RIS in strengthening ISAC security. Furthermore, it explores key optimization methodologies, technical challenges, and future research directions for integrating RIS with PLS to ensure secure and efficient ISAC in next-generation 6G wireless networks.