# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 3 Source Correlation Analysis

Table 6: VOSviewer clusters. Figure 4: VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding bibliographic coupling (see Table 6 for VOSviewer clusters). Distributed Artificial Intelligence Green 6G Pervasive Edge Computing Communication Networks in Energy-Efficient IoT Smart Industrial Urban Environments  
Sixth-generation communication technology advancements can significantly impact massive network infrastructures by the large-scale volume of network nodes [29], resulting in high energy consumption. Green Internet of Vehicle (IoV)-based intelligent transportation systems can configure sustainable vehicular communication, computation, traffic, and networking by diminishing electricity expenses and fuel consumption. Energy harvesting and vehicular networking technologies can be leveraged in green 6G IoV systems on a large scale by use of big data analytics, deep and machine learning algorithms, and cognitive computing. Onboard information service, routing design, network topology, traffic control and conditions, and connected IoV devices can optimize vehicular sensor integration and increase energy efficiency. Big-data-based intelligent decision algorithms can improve energy efficiency in terms of management, harvesting, and sharing across sustainable vehicular communication. Edge computing, traffic management, and 6G green IoV networking infrastructures are also instrumental in this respect. Network node computing and transmission capabilities [34] can be optimized in 6G network environments. Software-defined technology-based in-network computing can manage complex computing tasks \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline Main topics covered by the authors in each group & pear (IIoT network edge computing nodes and edge–cloud collaborative flexible computing resource allocation and sharing), violet (big-data-based intelligent decision and energy-efficient in-network computing task scheduling algorithms), blue-green (6G energy-efficient and energy-aware resource management), emerald (6G IIoT edge and cloud collaborative mechanisms for energy-aware decision support systems), amaranth (mobile-enhanced digital twin technologies for pervasive edge computing IIoT process intelligence) & pear (6G blockchain-based green IoT big data networks), violet (IIoT deployment in green 6G networks and environments), blue-green (energy-efficient IoT smart industrial urban computing environments), emerald (6G pervasive edge computing communication networks), amaranth (industrial cloud computing capabilities for energy efficient IoT network connectivity) & pear (38), violet (10), blue-green (32), emerald (34), amaranth (64) \\ \hline \end{tabular}
\end{table} 
Table 7: VOSviewer clusters. Figure 5: VOSviewer mapping of energy-efficient Industrial Internet of Things in green 6G networks regarding co-citation (see Table 7 for VOSviewer clusters). while reducing energy consumption by downsizing the data scale. Virtualization and container technologies are pivotal in computing task unified scheduling and programming, application-level service and in-network computing task scheduling, and data traffic processing in intelligent transportation systems. Sixth-generation energy-efficient and energy-aware resource management requires efficient computing and storage distribution and task offloading [12] in the large-scale IoT-fog environment. Energy-aware mechanisms leveraged throughout the distributed computing environment typically shape the quality of service in fog IoT networks. Performance-aware mechanisms can enhance fog computing energy efficiency in the 6G IoT area. By compressing artificial intelligence data shared across resource constrained IIoT wireless networks [66], spectrum usage, end-to-end latency, input redundancy and irrelevancy, and energy consumption can decrease while having low computational expenses.