# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 8 Discussion
Green 6G artificial intelligence IoT industrial wireless systems facilitate real-time data acquisition, compression, and reconstruction, determining data transmission performance by real-time closed-loop control, edge-cloud collaborative mechanisms, and predictive maintenance for energy efficiency. IIoT device data collection in relation to pervasive edge computing nodes leads to machine learning process-based energy consumption optimization [47] in increased industrial network uplink traffic. Edge computing federated learning techniques, radio access network spectrum resources, and distributed artificial intelligence can lead to energy efficiency and optimize computational performance in industrial 6G communication networks. Digital twin network adaptive intelligence supports real-time 6G scalable network operation monitoring, ensuring reliable wireless connectivity, while enabling green high network capacity, real-time environmental modeling, ultra-low latency, and lower-complexity devices by IoT perception and sensing technologies. Generative AI digital twins and 6G sustainable mobile wireless communication and integrated radio frequency sensing technologies can enable 6G network scalability and proactive planning, fostering seamless multi-connectivity, high-reliability communication, and intelligent computing operations. Adaptive intelligence 6G technologies can enhance ubiquitous wireless connectivity capabilities, low-power joint communications, and spatial perception and broadband services. Generative AI-enabled digital twin scalable network operations and end-to-end wireless systems foster system spectral and network energy efficiency, quantum-safe communications, and channel non-linearity optimization. High-speed 6G wireless network operations and green technologies for environmental sustainability decrease network energy consumption. Case Study: Fujitsu Data Application Simulations on Energy-Efficient Industrial Internet of Things in Autonomous Green 6G Networks  
We have chosen Fujitsu as the case study and carried out application simulations based on sensed data mapping related to energy-efficient Industrial Internet of Things in autonomous green 6G networks by harnessing 3D holographic and extended reality technologies for feature-rich devices and cyber-physical production systems in IoT digitized spaces. Holographic communication depends on network virtualization capabilities and IoT end device data, image, and digital signal processing performance, with mobile core network functions as distinct microservices (disaggregation). Edge computing technologies enable IoT sensor and video data collecting and processing management across end-to-end cloud-native networks. Fujitsu integrates highly flexible open energy-efficient mesh networks and green technology (considering millimeter-wave access system advancements)for intelligent orchestration efficiency by multi-vendor and -layer operation automation. Sensor- and drone-technology-based factory floor work and production automation performance develop on plant and equipment condition inspection for preventive maintenance by autonomous robots. Spatial augmentation and digital twin technologies can be harnessed in smart factories for energy supply and demand stabilization. Fujitsu develops digital twin high-performance 6G networks for energy efficiency, considering ever-changing situational conditions by use of real-time sensor- and camera-based machine control, production-process-unattended operation, and high-capacity data communication-based wireless devices. IoT camera and sensor data collecting and processing, high-speed data transfer environmental change detection, and power consumption mitigation can be achieved by high-capacity data communication-based wireless devices, software-defined network virtualization functions, and edge computing and green technologies across geo-distributed end-to-end ICT infrastructures.