# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 3 Source Correlation Analysis
Convolutional-neural-network-based image classification simulations can result in accuracy performance across 6G intelligent IoT networks. Significant energy resources enable cloud-computing-based distributed IoT network operations and unmanned aerial vehicle communications with regard to cooperative sensing, wireless vehicular data sharing, and input transmission. Internet-of-Everything-based smart services develop on artificial intelligence 6G wireless communication systems [57] by integrating edge computing, holographic beamforming, and backscatter communications for IoT networks. Mobile communication network management automation systems impact artificial intelligence 6G IoT-based wireless robust connectivity. Spectrum technologies, machine learning techniques, and molecular and quantum communication networks can optimize low communication latency. IoV network resource allocation computing and 6G intelligent transportation systems (51) can optimize vehicular traffic and mobility pattern prediction and collision and traffic jam warning. Collaborative industrial artificial-intelligence-based smart IoT device, manufacturing, and factory systems can improve production processes and reduce energy consumption by real-time sensor data collection and communication services in distributed network environments. Artificial intelligence edge-side data compression can cut down transmission latency [66], with computational complexity being pivotal in data aggregation processes. Multi-layer deep- and machine-learning-based large-scale data compression nonlinear mapping capabilities can eliminate data redundancy by integrating long short-term memory, recurrent, and convolutional neural networks. Weight fine-tuning technologies and principal component and linear discriminant analysis can be harnessed across distributed IIoT devices for feature extraction, spectrum, storage, and computing resource consumption as well as high-dimensional data visualization. Computational workloads should be adequately distributed throughout pervasive edge-computing-assisted IIoT networks to decrease energy usage [47] by mobile edge and cloud computing task processing capabilities, optimized data storage and analysis, and heterogeneous IIoT service quality. Energy-centric design of distributed artificial intelligence 6G pervasive edge computing communication networks develops on IIoT device power supplies, energy resources, and computational capabilities. Extended reality, digital twin, and blockchain technologies, artificial intelligence algorithms, and IoT shape 6G communication systems by holographic and ubiquitous connectivity [44] while generating energy efficiency and spectrum allocation issues. IoT decision support and intelligent environmental systems and IIoT energy consumption and management techniques [62] can harness deep and machine learning algorithms for real-time decision automation in travel optimization due to IoT intelligent device communication complexity. Artificial neural networks, power consumption and energy allocation algorithms, and data mining and energy management techniques [64] shape sustainable energy efficiency and power generation. Industrial IoE, Internet of Drones (IoD), IoV, and Environmental Internet of Everything (EioE) are pivotal in energy management and harvesting techniques, energy consumption management, and green power generation and sustainability in smart grid and industrial equipment environments. Our analyses prove that energy-efficient environmentally aware wireless communication technologies can optimize power efficiency and reduce system energy consumption and emissions [13; 28; 29; 33; 38; 53] throughout power plants and grids. Sixth-generation sensor-based IoT green sustainable IIoT networks and wireless power transmission techniques integrate renewable and sustainable energy sources through big-data-driven IoT device sensor connectivity.