http://hdl.handle.net/10453/148699 2026-06-26T04:01:27Z http://hdl.handle.net/10453/195403 Title: An Overview of Principles, Enablers, Metrics and Emerging Technologies for Resilient Networks Authors: Chemalamarri, VD; Abdollahi, M; Bryant, A; Abolhasan, M; Owen, R Abstract: There are approximately 5.78 billion users relying on mobile networks to access a wide range of digital services. Hence, the resilience of these communication networks is of the highest importance. In this paper, we define network resilience and outline key architectural design principles and their enabling technologies. We further describe relevant metrics for assessing network performance and examine emerging technologies, such as the integration of terrestrial and non-terrestrial networks, artificial intelligence-enabled network management and optimization, as well as innovative concepts like core network disaggregation and non-IP-based architectures. We identify current challenges and highlight critical research directions for the advancement of resilient mobile networks. 2026-01-01T00:00:00Z http://hdl.handle.net/10453/195323 Title: In-Band Network Telemetry for End-to-End Service Performance Monitoring in Broadband Networks: A Survey and Deployment Feasibility Analysis Authors: Altaf, T; Abolhasan, M; Owen, R; Barton, I; Guruge, T; Franklin, DR Abstract: End-to-end performance monitoring is essential for ensuring service reliability and operational visibility in modern telecommunications networks. Traditional monitoring techniques often rely on out-of-band measurements, which provide limited visibility into real-time packet behavior across complex network infrastructures. In-band telemetry has emerged as a promising approach that enables performance information to be collected directly from packets as they traverse the network. This article presents a comprehensive survey of in-band telemetry techniques and evaluates their applicability to end-to-end performance monitoring in broadband networks. The survey first examines header stamping approaches, including mechanisms such as In-band Network Telemetry (INT), In-situ Operations, Administration, and Maintenance (IOAM), and related implementations. It then reviews packet marking and selective sampling techniques that aim to reduce measurement overhead while maintaining monitoring accuracy. Hybrid approaches that combine multiple telemetry mechanisms are also discussed. Building on this taxonomy, the paper analyzes the feasibility of deploying in-band telemetry across multi-segment telecommunications environments using broadband services as a representative use case. The analysis highlights several limitations in current mechanisms, including the absence of standardized Layer-2 telemetry support, the assumption of single-domain deployment, and the lack of mechanisms for sharing telemetry information across adjacent network segments. Finally, the paper identifies key open challenges and research directions required to enable scalable, interoperable, and operationally viable end-to-end telemetry solutions for future broadband networks. 2026-01-01T00:00:00Z http://hdl.handle.net/10453/195283 Title: Codebook prior-guided hybrid attention dehazing network Authors: Huang, L; Zheng, H; Pan, L; Su, Z; Wu, Q Abstract: Transformers have been widely used in image dehazing tasks due to their powerful self-attention mechanism for capturing long-range dependencies. However, directly applying Transformers often leads to coarse details during image reconstruction, especially in complex real-world hazy scenarios. To address this problem, we propose a novel Hybrid Attention Encoder (HAE). Specifically, a channel-attention-based convolution block is integrated into the Swin-Transformer architecture. This design enhances the local features at each position through an overlapping block-wise spatial attention mechanism while leveraging the advantages of channel attention in global information processing to strengthen the network's representation capability. Moreover, to adapt to various complex hazy environments, a high-quality codebook prior encapsulating the color and texture knowledge of high-resolution clear scenes is introduced. We also propose a more flexible Binary Matching Mechanism (BMM) to better align the codebook prior with the network, further unlocking the potential of the model. Extensive experiments demonstrate that our method consistently outperforms the second-best methods by a margin of 8% to 19% across multiple metrics on the RTTS and URHI datasets. The source code has been released at https://github.com/HanyuZheng25/HADehzeNet. 2025-10-01T00:00:00Z http://hdl.handle.net/10453/195195 Title: Cyber–physical resilience and security strategies for renewable energy-driven IoT power grids Authors: Li, TT; Huang, Y; Hua, Y; Hu, T; Yang, H; Alhazmi, M; Alsaif, S; Liu, G Abstract: The increasing penetration of renewable energy resources, coupled with the rapid proliferation of Internet of Things (IoT) technologies, has transformed the architecture of modern power grids into highly interconnected and intelligent systems. While these advances improve sustainability, flexibility, and operational efficiency, they also expand the attack surface, exposing critical infrastructures to sophisticated cyber and physical threats. This paper proposes a cyber–physical resilience and security framework designed to safeguard IoT-enabled power grids under adversarial conditions. First, a vulnerability assessment methodology is developed that integrates renewable generation variability, IoT communication dependencies, and network topology to identify systemic weak points across both cyber and physical domains. Second, resilient control strategies are introduced by combining distributionally robust optimization with multi-agent reinforcement learning to achieve secure energy allocation, adaptive task scheduling, and rapid system recovery under uncertainty. Third, a set of security strategies is proposed, including blockchain-based authentication for trustworthy data exchange, lightweight cryptographic techniques for IoT nodes, and deep learning-driven intrusion detection to counter evolving attack patterns. This study highlights the necessity of integrating optimization, learning-based control, and secure system design to enable the next generation of cyber–physically resilient renewable energy-driven IoT power grids. 2025-12-01T00:00:00Z