# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 3 Source Correlation Analysis
Internet of Energy (IoE) comprises smart and high-voltage grids, 6G communication technologies, and renewable energies [13], integrating decentralized network control and power grid system monitoring, intelligent energy packaging management, and IoT measurement infrastructure with regard to electricity production and distribution. IoE technologies can decrease energy consumption, handle energy reserves, and manage energy generation and distribution, being instrumental in reducing carbon emissions. IIoT device multi-source heterogeneous data compressibility can be used to carry out data aggregation [66] by use of cloud manufacturing networks and industrial cloud computing capabilities. IIoT data sharing and computing devices, 6G IIoT edge and cloud collaborative mechanisms, and production line operation control actuators can be also harnessed. IIoT network edge computing nodes can assess operation status and abnormal events by multi-dimensional heterogeneous data through sensor instrumentation and control. Edge-cloud collaborative flexible computing resource allocation and sharing can optimize data transmission analysis, efficiency, prediction, connectability, inference, and interoperability. The massive volume of distributed devices [47] may intensify computational workloads and energy consumption significantly. Deep learning artificial intelligence and federated learning techniques can reduce energy consumption and increase computation capacity, integrating distributed learning node capabilities, augmented and virtual network functions, and customized communication networks. Insufficient computation capability and battery-powered energy [43] affect edge-centric 6G network data offloading across IoT devices. Energy-efficient algorithm and green computing technologies enable 6G edge network data offloading, cutting down energy consumption and latency in computation offloading in 6G IoT networks. Smart sensors and battery-powered renewable IoT node-based ambient energy sources can reduce computation tasks, resulting in low energy consumption. Network function virtualization and slicing technologies, real-time sensing data and inference task execution, and software-defined and self-organized networking can enhance renewable energy utilization [37]. Movable infrastructure and mobile network power consumption, intelligent edge energy costs, and intelligent wireless network performance degradation in artificial-intelligence-based IIoT can thus be reduced. Context-aware electrical power generation management, energy management, harvesting, and power consumption techniques and power supply monitoring improve energy efficiency throughout smart girds in 6G IoT environments [64]. This is typically carried out using deep refinement learning and particle swarm optimization algorithms. Renewable energy generation, edge-based service scheduling, and blockchain-based transitive energy and solar power management can minimize power consumption in smart industrial equipment and manufacturing environments by deep learning and clustering techniques. Green energy computing and big-data-based intelligent power metering can be deployed for smart grid and transportations. Our analyses prove that energy-efficient environmentally aware wireless communication and environmental pollutant reduction technologies determine natural environment changes, energy utilization efficiency, and environmental sustainability [13; 28; 29; 54; 55; 62; 64] in low-carbon sustainable energy in green environments. We also clarify the importance of environment mapping algorithms in IoT decision support and intelligent environmental systems. Sustainable Energy Efficiency and Green Power Generation in Distributed Artificial Intelligence 6G Pervasive Edge Computing Communication Networks  
Artificial intelligence techniques, edge intelligence systems, and fog intelligence [48] can configure low-latency IoT services across fog-node-based mobile intelligent networks.