# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 3 Source Correlation Analysis
Extended-reality-technology-related compressed big data and stringent latency requirements can challenge transmission efficiency across edge-cloud collaborative 6G IIoT and resource-constrained wireless networks. Complex data processing supports ubiquitous intelligence in artificial intelligence model design by deep and machine learning algorithms. Distributed artificial intelligence 6G pervasive edge computing communication networks for IIoT [47] can enhance predictive maintenance, computing and networking resources, and operational efficiencies. Machine learning algorithms and cloud computational capabilities can optimize pervasive edge computing IIoT process intelligence across 6G communication and automation networks for energy efficiency. Edge computing can optimize 6G network data processing capabilities [43] and reduce energy consumption. The smart IoT device data volume and ever-reducing size of the IoT system energy-constrained device can impact 6G edge network technical design with regard to computationally extensive task offloading. Long short-term memory-based deep recurrent neural algorithms can thus be used. Sustainable edge intelligence can optimize sensing and computing capabilities of intelligent connected vehicles across green smart 6G networks for vehicle-to-everything communication and autonomous driving traffic fluctuations [37] while decreasing carbon emissions via renewable energy power. Cooperative perception and inference tasks facilitate sensor data fusion in green smart 6G network computation traffic and route planning in transportation systems by traffic demand monitoring and prediction. Consequently, carbon emission and renewable energy consumption are reduced. Intelligent urban computing and industrial systems can assess energy-efficient performance [62] by ant colony and particle swarm optimization algorithms. Smart transportation, environmental, and industrial supply chain management can decrease energy consumption by supervised machine learning and unsupervised recommender algorithms. Bio-inspired, genetic, and evolutionary algorithms are pivotal in energy-aware decision support system and smart grid management across energy-efficient IoT smart industrial urban computing environments. Our analyses prove that the long-term sustainability and environmental footprint of widespread adoption of IIoT in green 6G networks and across low-carbon sustainable energy in green environments [13; 37; 63] are related to low-carbon sustainable development and resource allocation [28; 53], IoT-energy- and cloud-computing-based pollution and carbon footprint decrease [53; 63], renewable energy power carbon emission reduction [13; 28], energy generation and distribution management [28; 37; 53], and smart grid and zero-carbon technology-driven distributed energy generations [13; 37; 63]. Energy-Efficient Algorithm and Green Computing Technologies in Smart Industrial Equipment and Manufacturing Environments  
Edge intelligence, IoT data sensing, reconfigurable intelligent surfaces, blockchain technologies, and reliable low-latency communications [48] shape 6G network management. Sixth-generation wireless communication and vehicular IoT intelligent and autonomous networks as well as data-centric intelligent systems can optimize seamless mobile traffic capability. Edge and cloud computing services, large-scale device connectivity density, spec trum availability, and network latency assist in this respect. Multiple target decomposition evolutionary algorithms can carry out multiple data stream task scheduling [34], decreasing system energy consumption and network transmission traffic. Task scheduling can enhance resource utilization by the use of virtual machines and containers. Energy-efficient in-network computing task scheduling algorithms can deploy 6G network equipment transmission and computing capabilities in virtual machine environments. Thus, network traffic and data processing service scheduling are improved.