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Adaptive mobile VR content delivery for industrial 5.0
Mobile virtual reality (VR) is expected to be a key component of the next-generation industrial internet-of-things, which uses immersive technologies to boost virtualization and facilitate human-machine collaboration in Industry 5.0. In this paper, we ...
Optimal graph partitioning for time-sensitive flow scheduling towards digital twin networks
The growing maturity of Digital Twin (DT) technology represents a quantum leap towards the realisation of Industry 4.0 and beyond. DT refers to virtual representations (in a virtual space) of physical objects/processes/systems (in a physical space), ...
MicroInfer: an edge deep-learning inference framework for industry IoT
To address the difficult deployment, high memory overhead, and poor portability of edge deep learning frameworks in industry IoT, we propose MicroInfer which is an edge deep learning inference framework for Microprocessors. MicroInfer employs a ...
Learning to optimize computation offloading performance in multi-access wireless networks
In this paper, we investigate computation offloading in a multi-access wireless network, which supports both cellular and WiFi connectivity between a mobile user (MU) and the edge server. The MU decides to process an arrived computation task locally at ...
Transformer-driven multi-agent deep reinforcement learning based point cloud video transmissions
The point cloud videos, a medium for representing natural content in AR/VR with point clouds, have attracted a wide range of attention for its characteristics and have the potential to be the next generation of video technology. Given the high data ...
Space spider: a hyper large scientific infrastructure based on digital twin for the space internet
With its advantages of low latency and global coverage, the Low Earth Orbit (LEO) satellite constellations can form an effective complement to the terrestrial 5G/6G mobile communication system and provide infrastructure support for broadband access and ...