# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 7 Results  
Deep- and machine-learning-based green communications can optimize energy efficiency [75] and alleviate fossil fuel usage in 6G network infrastructures and connected terminals [56] by dynamic energy harvesting while improving power control, accuracy rate, and network management. Green computing and 6G IoT communication and heterogeneous wireless networks [63] can enable low-carbon sustainable development and resource allocation by adaptive boosting algorithm-based data classification accuracy and spectrum utilization efficiency. Energy harvesting technologies and green IIoT can enhance energy efficiency, transmit power control, and sustainable machine-to-machine communication services [30] throughout unmanned aerial vehicle (UAV) networks. Ultrascale multiple antennas and reconfigurable intelligent surfaces [61] are pivotal in the 6G energy-efficient green wireless communication network design. Autonomous network wireless technologies require data-intensive 6G Internet of Everything wireless communication networks in smart resource distribution [31] by artificial intelligence energy-efficient communication autonomous networks and device features and functions. Green sustainable IIoT can reduce system energy consumption and emissions [33] by efficient deployment of equipment resources. Federated-learning-based ambient backscatter systems can enhance resource scheduling schemes, while intelligent algorithms can find a solution to offloading task issues. Intelligence-driven self-organizing energy-efficient green resource allocation in dynamic complex environments and 6G heterogeneous networks [52] develop on reliable IIoT services by harnessing deep reinforcement learning algorithms. IoT devices require massive volumes of energy consumption [54], impacting the electric grid load and bringing about natural environment changes. Green energy wireless charging algorithms can significantly power IoT devices. Energy harvesting technologies can extend 6G IoT device lifetime [49], facilitating green sustainable communication through transmission effectiveness by use of deep reinforcement learning and location preserving algorithms. Green energy-efficient ubiquitous artificial intelligence 6G Internet of Everything technologies are based on edge cloud computing [41] due to mobile device network traffic development and radio map network perception. Ubiquitous and seamless green communication and interconnection among huge volumes and the high mobility of 6G-enabled IoT devices [60] can decrease energy consumption through reduced end-to-end delay, cluster-based data dissemination, and a significant data rate. Whale optimizer algorithms and spotted hyema optimizers can also be harnessed in this respect. Data analytics technologies, computational optimization algorithms, and mobile edge computing [53] can be deployed for power-efficient structures, energy efficiency and performance, and streamlined energy resource management in smart cities. Green urban operations can optimize smart city equipment energy and power efficiency and consumption, reducing greenhouse gas emissions. IoT energy and cloud computing can optimize urban operations in green city building and in pollution and carbon footprint decrease in terms of smooth energy, smart grids, and electrical flow. IoT sensing devices can strengthen energy harvesting techniques [45] in IoT networks and 6G environments. IoT devices require cellular network connections by various frequency bands in 6G IoT networks, and energy exhaustion should be avoided. Network automation and robotic technologies and deep and machine learning algorithms [46] configure 6G IIoT environments. Sixth-generation IoT communication systems and devices [67] can minimize energy consumption. By intelligent offloading, fog computing technology [32] can reduce IIoT device energy consumption. Industrial network computation offloading is pivotal in fog device IIoT data distribution through reinforcement learning techniques and controller-based device adaptation.