XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics

EU COST ACTION ON FUTURE GENERATION OPTICAL WIRELESS COMMUNICATION TECHNOLOGIES -NEWFOCUS CA19111

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Big Energy Data Management for Smart Grids—Issues, Challenges and Recent Developments

Urban areas suffer from tremendous pressure to cope with increasing population in a city. A smart city is a technological solution that integrates engineering and information systems to assist in managing these scarce resources. A smart city comprises several intelligent services such as smart grids, smart education, smart transportation, smart buildings, smart waste management and so on. Among all these, smart grids are the nucleus of all the facilities because these provide sustainable electrical supply for other smart services to operate seamlessly. Smart grids integrate information and communication technologies (ICT) into traditional energy grids, thereby capturing massive amounts of data from several devices like smart meters, sensors, and other electrical infrastructuresSCADA. The data collected in smart grids are heterogeneous and require data analytic techniques to extract meaningful information to make informed decisions. We term this enormous amount of data as big energy data. This book chapter discusses progress in the field of big energy data by enlisting different studies that cover several data management aspects such as data collection, data preprocessing, data integration, data storage, data analytics, data visualisation and decision-making. We also discuss various challenges in data management and report recent progress in this field. Finally, we present open research areas in big data managementBig data management especially in relation to smart grids