# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 3 Source Correlation Analysis
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Source** & **Applied Research** \\ \hline
16 & [65] & Digital twin AI algorithms enable energy-efficient 6G network deployment in smart factories based on mobile-enhanced big data edge computing–cloud collaborative mechanisms for green communication and network performance optimization \\ \hline
17 & [52] & Fifth-generation Industrial Internet of Things (IIoT) heterogeneous network intelligence-driven green energy-efficient resource allocation mechanisms based on deep reinforcement learning algorithms \\ \hline
18 & [61] & Integrated relative energy efficiency can assess traffic profiles and wireless network capacities for green communications \\ \hline
19 & [66] & Sixth-generation edge–cloud collaborative industrial-IoT-learning-based data compressions and transmissions for spectrum efficiency increase and system latency mitigation \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._communication networks, and energy-efficient IoT smart industrial urban computing environments [33; 38; 44; 48; 59]. Bibliographic coupling correlations (Figure 4) show that massive wireless energy-efficient sustainable network automation and distributed federated learning shape 6G green communications and IIoT fog networks. Mobile device computational, distributed sensing, and holographic beamforming capabilities can ensure secure and privacy-preserving communication mechanisms [55; 57; 58] with regard to green IIoT data in 6G IoT energy-efficient fog computing networks. Sixth-generation edge computing, wireless spectrum, and digital twin technologies; federated learning, 6G wireless communication, and multi-agent \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline Main topics covered by the authors in each group & pear (collaborative industrial artificial-intelligence-based smart IoT devices), maroon (green-network-based energy efficient systems), orange (6G-network-based energy efficient and green artificial intelligence IoT industrial wireless systems), violet (energy-aware resource management and spectrum sensing algorithms), turquoise (green 6G artificial intelligence IoT industrial wireless systems), azure (large-scale industrial production-based IoT device technologies) \\ \hline Research focus reasons & pear (6G AI green computing and autonomous wireless communications), maroon (deep- and machine-learning-based green communications), orange (edge–cloud collaborative 6G IIoT networks), violet (artificial intelligence energy-efficient communication autonomous networks), turquoise (massive wireless energy-efficient sustainable network automation), azure (edge computing IIoT process intelligence) \\ \hline The number of research groups having shared interests & pear (25), maroon (24), orange (23), violet (28), turquoise (26), azure (10) \\ \hline \end{tabular}
\end{table} 
Table 4: VOSviewer clusters. Figure 2: VOSviewer mapping of energy-efficient Industrial IoT in green 6G networks regarding co-authorship (see Table 4 for VOSviewer clusters). systems; and collaborative and distributed computing intelligence tools [14; 56; 64] can consolidate privacy and cybersecurity across energy-efficient IoT smart industrial urban computing environments. Secure and resilient sustainable energy sources require green IoT, immersive simulation, and cloud-based computing technologies, edge intelligence and mobile communication network management automation systems, and environment mapping and spectrum sensing algorithms [40; 50; 61] in 6G sensor-based energy-efficient IoT smart industrial urban environments. Green 6G IIoT security and privacy concerns and issues can be solved by leveraging energy-efficient 6G IoT fog computing and environmentally aware wireless communication technologies [13; 45; 46] throughout artificial intelligence energy-efficient communication autonomous and clustered wireless sensor networks.