Considering the growing interest in adopting distributed fiber optic sensing (DFOS) technology for telecom infrastructure monitoring and protection, ITU-T SG15 recently approved the G.681 Recommendation to address the demand. This standard represents a major milestone and the first step toward building a viable DFOS ecosystem for the telecom industry. It specifies the optical interface parameters for utilizing DFOS for in-service terrestrial networks. This paper discusses DFOS application examples, provides an overview of the G.681 Recommendation, and offers readers insights and rationale behind the development of the standard.
The talk provides a historical review, discusses current developments and forward-looking perspectives on the optical access networks evolution, while comparing the originally chartered 2007 and 2016 FSAN roadmaps with the progress subsequently achieved in practice.
Edge computing has become indispensable in the landscape of the Internet of Things (IoT), enabling immediate data processing at or near the data source, thereby reducing latency and enhancing operational efficiency. Microsoft Azure stands out among cloud service providers by offering a comprehensive suite of tools specifically designed for deploying edge computing solutions. Leveraging Azure IoT services and Edge modules empowers organizations to extend their computing capabilities from centralized cloud environments to the edge of their networks. The integration of Azure's Edge computing capabilities into IoT deployments addresses several critical aspects essential for modern digital ecosystems. Primarily, it facilitates processing data closer to its origin, which proves beneficial in scenarios requiring rapid responses, such as industrial automation, remote monitoring, and smart cities. This proximity minimizes latency and optimizes overall system performance by reducing bandwidth usage. Azure's versatile toolset supports a wide array of IoT applications, including predictive maintenance, anomaly detection, real-time analytics, and AI inferencing. These capabilities enable enterprises to derive actionable insights from data in near real-time, enhancing decision-making processes and operational agility.However, the adoption of Azure Edge computing presents challenges, particularly in managing edge devices distributed across various geographical locations. Robust security protocols, reliable connectivity solutions, and efficient device management strategies are crucial to ensure data integrity, scalability, and resilience. In summary, Azure's Edge computing solutions represent a significant advancement in IoT deployments, empowering organizations to achieve higher levels of operational efficiency and intelligence. Azure's commitment to innovation and its ecosystem of Edge computing tools position it as a key facilitator of next-generation digital transformation initiatives globally
The integration of Artificial Intelligence (AI) in educational environments presents unique opportunities and challenges for student engagement, attitudes towards learning, and the cognitive processes involved in education.This paper explores the efficacy of AI tools in enhancing collaborative learning experiences, facilitating personalized learning paths, and potentially transforming traditional pedagogical approaches.By employing a quasi-experimental design, this study analyzes how AI influences learning dynamics across different collaborative learning settings.
This work reports on the development, deployment, and continuous field operation of a federated trusted node quantum network in service to a separate, quantum-resistant classical network. In this demonstration, we developed the infrastructure needed to connect commercial off-the-shelf quantum key distribution (QKD) systems into a four-site, multi-vendor mesh network shared by Amazon Web Services (AWS) and Verizon Communications in the Boston metropolitan area. Encrypted network links leverage QKD-enabled protocol suites, including IPsec and MACsec with data transfer rates up to 100 Gbps connecting on-premises locations to the cloud edge via the AWS Direct Connect service. Detailed health and performance monitoring data gathered over half a year allow for gap analysis with today’s current commercial quantum network technologies. Coupled with symmetric encryption key demand forecasts, this sets the foundation for defining the paths forward to meet carrier-grade network redundancy and resilience requirements.
