# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 1 Introduction
### The Objectives of the Study  
Through large-scale device interconnections, 6G wireless systems can bring about massive quantities of traffic data, leading to serious spectrum scarcity and system latency [66]. Our objective is to show that deep- and machine-learning-based data compression technologies can result in efficient input transmission across edge-cloud collaborative 6G IIoT and smart manufacturing networks in production lines. Smart IIoT devices can generate big data, causing intense computing load and unforeseeable processing delays. Energy is consumed by wireless transmission, and sensors and end nodes do not have enough computing resources, storage space, energy, or spectrum capabilities. Mobile-enhanced digital twin technologies can simulate 6G edge computing green communication wireless network planning, performance, and deployment [65], reducing operational costs. Artificial intelligence algorithms can be leveraged for energy-efficient 6G network deployment in smart factories by self-evolution capabilities and cloud collaborative mechanisms through complex data handling.  
This is the first systematic review covering energy-efficient Industrial Internet of Things in green 6G networks due to the relevance of deep-learning-based 6G data-driven network and green 6G multi-access edge computing technologies in cyber-physical system-IoT industrial environments by machine learning clustering, energy harvesting techniques, and power allocation and transmission algorithms for energy aware scheduling algorithm-based 6G green communication systems and deep-neural-network-based energy-efficient transmission optimization. Sixth-generation wireless IoT networks and communication technologies [68] can be leveraged for high transmission rates, effective resource optimization, and energy, spectrum, bandwidth utilization, and power efficiency. Deep-learning-based 6G data-driven network and wireless communication technologies [69] can increase speed and decrease latency, enhancing end-to-end behavior. One-shot federated learning optimizes green and sustainable data-driven IoT connectivity [70], curtailing resource utilization and environmental footprint, energy consumption, and latency by IoT device knowledge sharing and lifespan extension. IIoT wireless environments can integrate sensors, actuators, and controllers [71], configuring air-to-ground links and wireless channels by unmanned aerial vehicles (UAVs), green device-to-device communication techniques, channel allocation parameters, and reconfigurable intelligent surfaces (RISs) for deep-neural-network-based energy-efficient transmission optimization.  
The manuscript comprises an outline of the methodology (Section 2), source correlation analysis (Section 3), distributed artificial intelligence green 6G pervasive edge computingcommunication networks in energy-efficient IoT smart industrial urban environments (Section 4), energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments (Section 5), sustainable energy efficiency and green power generation in distributed artificial intelligence 6G pervasive edge computing communication networks (Section 6), results (Section 7), discussion (Section 8), case study (Fujitsu data application simulations on energy-efficient Industrial Internet of Things in autonomous green 6G networks) (Section 9), conclusions (Section 10), theoretical contributions to the literature (Section 11), practical contributions to the literature (Section 12), and limitations and further directions of research (Section 13).