# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 8 Discussion  
Sustainable communication networking and vehicular edge computing, intelligent traffic and charging management, and scheduling optimization [29] can reduce energy consumption by green 6G IoV networking technologies and by dynamic energy sharing and harvesting. Sixth-generation wireless signal networking, sensing, and communication integration develops on wider bandwidth, antenna array dense distribution, and higher frequency bands. IoV-based vehicle-to-everything communication can shape suitable speed adjustment, traffic sensor data collection, and route planning, saving energy consumption. It also enables high-accuracy location and swift interruption recovery across the surrounding environment. Energy consumption can be minimized in optimized offloaded task transmission and processing and resource allocation across large-scale distributed networks and low-complexity smart traffic management in vehicular edge and cloud computing environments by artificial-intelligence-based self-learning algorithms. Significant resource utilization and reduced energy consumption [34] typify energy-efficient computing in 6G networks. In-network computing enables decreased cloud-side system energy consumption by network data plane application-layer processing functions. Sixth-generation energy-efficient in-network computing leverages hypervisors and containers across application task unified operating environments. Cloud servers and network nodes can integrate data processing tasks. Network node computing resources can cut down network transmission overhead, data processing pressure, and energy consumption. Clustered wireless sensor networks can improve energy efficient communications [12] by reducing network traffic, transmission emissions, and routing delay. The resource allocation decision process across 6G IoT energy-efficient fog computing networks and distributed heterogeneous devices is enabled by precise network data and deep and machine learning algorithms for transmission energy efficiency and optimization. Diverse computation task, networking, and latency limitations should be analyzed in workload distribution to attain energy efficiency without computing device overloading for coherent resource allocation. Internet of Energy and smart control systems, together with distributed energy generations by use of smart grids and zero-carbon technologies [13], advance low-carbon sustainable energy in green environments. Intelligent energy system interoperability and electrical mobility facilitate automatic consumption enhancement and boosts network efficiency management in terms of consumption and distribution. Electric energy consumption and misallocation can shape greenhouse gas emission by power plants and grids, data management networks, and large-scale renewable generations. IoT wireless network systems require deep and machine learning algorithms, supervised and unsupervised, deep reinforcement, and federated learning algorithms as well as particle swarm and ant colony optimization algorithms in artificial network nodes. Energy efficient IoT network connectivity and resource management assist reconfigurable intelligent surfaces and self-operable communication systems [15] in energy efficient network and spectrum management and across smart grids. Green and sustainable federated learning- and blockchain-based IIoT [51] can shape autonomous vehicles and unmanned aerial vehicles in intelligent transportation systems. The multi-layer and edge computing cloud architecture in green sustainable factories develop on 6G mobile networks and unmanned aerial vehicle systems, advanced artificial intelligence techniques, mobile edge computing services, decentralized remote monitoring, and federated learning system computation resources. Data compression algorithms can enhance end-to-end reliability, mapping, and latency [66] through 6G edge computing IIoT network function and lightweight virtualization.