# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 1 Introduction
### The Research Gap of the Study  
The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial IoT (IIoT) in terms of practical integration challenges and compatibility issues between existing industrial systems and emerging 6G technologies. Energy-optimized mobile and radio access networks [21] can reduce energy consumption and enhance sustainable green communication, power usage, and resource management across mobile devices. Sixth-generation cellular networks can deploy zero-energy cellular Internet of Things devices [22] using beamforming, wireless information and power transfer management, energy harvesting, and backscatter communication techniques. Internet-of-Everything-enabled 6G networks can improve energy efficiency and resource allocation [23] in terms of localization, coverage, convergence, connectivity, latency, and resilience. We show how distributed AI green mobile network automation, spatial perception and sensor fusion algorithms, and dynamic spectrum sharing and energy harvesting technologies can be deployed in integrated wireless sensing, distributed computing, and mobile communication for environmental detection. Sixth-generation radio and spatial computing technologies, digital twin extended reality devices, and visual localization techniques can be leveraged in object recognition, 3D graphics rendering, and scene understanding across cloud virtualized networks. Real-time environmental, system-level semantic awareness, and contextual sensing, orthogonal multiple and spectrum access efficiency scaling, optimized network traffic and power management, and 6G precise positioning and mobile communication operations develop on edge computing and reconfigurable intelligent surface technologies, edge cloud processing, and federated reinforcement learning algorithms for cross-node machine-learning-based wireless system operation and contextual sensing performance. We clarify that generative AI-native 6G end-to-end communication technologies and federated learning algorithms can optimize scalable network and device distributed inference, connected intelligent edge cloud processing distribution, and reconfigurable intelligent surfaces for sustainable power efficiency. Generative AI 6G mobile wireless communication and green-integrated high-resolution sensing technologies are pivotal in connected intelligent edge and efficiently distributed computing across energy-efficient network architectures, lowering power consumption. Quantitative analysis and empirical evidence demonstrate the energy efficiency improvements or cost benefits of implementing IIoT in green 6G networks. Strategies and technologies can ensure secure and privacy-preserving communication in green 6G IIoT networks. Technical standards, protocols, and architectures are pivotal for the deployment of IIoT in green 6G environments. The environmental impact of deploying green 6G networks can support sustainable industrial practices at scale. The core contribution is by showing how green 6G artificial intelligence IoT industrial wireless and network-based energy efficient systems integrate deep- and machine-learning-based green communications across 6G IIoT environments. Green 6G multi-access edge computing and vehicle-to-everything technologies [24] can mitigate energy use and improve resource utility by machine learning clustering. Green IoT networks integrate wireless sensor and cloud computing technologies, big-data-driven metering infrastructure, data centers, enhanced signal bandwidth, and machine-to-machine communication [25] to decrease energy consumption across smart grids based on predictive analytics-based renewable energy integration and edge- and fog-computing-driven distributed intelligence. We also clarified how 6G network infrastructures develop on green IoT and cloud-based computing technologies.