# Energy-Efficient Industrial Internet of Things in Green 6G Networks
## 2 Methodology
\begin{table}
\begin{tabular}{p{42.7 pt} p{284.5 pt} p{284.5 pt}} \hline \hline
**No.** & **Source** & **Applied Research** \\ \hline
1 & [39] & Sixth-generation IoT Radio-over-Fiber (RoF) system flexibility and efficiency across high-density heterogeneous devices by use of artificial intelligence (AI)-based smart algorithms \\ \hline
2 & [41] & Air-space-ground-sea AI 6G integrated network green architecture, radio and traffic map network perception green solutions for efficient AI-based green fusion, and KubeEdge wireless green management platform for node containerization and wireless connection expansion to carry out joint scheduling and resource orchestration \\ \hline
3 & [32] & Cost-efficient computation offloading (CeCO)—an industrial network computation offloading framework with network regulation master fog controller and fog device IIoT data distribution, and IIoT device weighted energy-delay cost sum optimization function design \\ \hline
4 & [33] & Intelligent-algorithm-based ambient backscatter system for resource scheduling scheme and task offloading optimization to reduce energy consumption and latency, while constituting a federated learning and asynchronous advantage actor-critic algorithm-based Markov decision process \\ \hline
5 & [34] & Sixth-generation energy-efficient in-network computing for network function integration in a general computing platform that serves as a network node and configures a unified hypervisor and container-based application task operating environment, significantly reducing data center energy consumption \\ \hline
6 & [43] & Long short-term memory deep recurrent neural networks in next-generation 6G edge network design for green computation in terms of energy consumption joint optimization Markov decision process \\ \hline
7 & [35] & A distributed federated learning (DBFL) framework for distant device connection in mobile edge computing architectures in a clustering-protocol-based distributed manner, overcoming energy efficiency issues \\ \hline
8 & [54] & Green energy wireless charging algorithms can efficiently power IoT devices by multi-green base station joint accumulative charging schemes \\ \hline
9 & [63] & A 5G-technology-based heterogeneous wireless network resource virtualization architecture for green computing with regard to IoT data classification \\ \hline
10 & [45] & An extended Kalman filtering method for 6G IoT sensing devices can predict harvesting power \\ \hline
11 & [37] & Blockchain, green ubiquitous IoT wireless spectrum sharing, and 6G mobile communication technology for energy efficiency \\ \hline
12 & [14] & Energy-aware resource management, 6G-enabled cell-free massive MIMO networks, and green communication capabilities can provide optimized network coverage and power based on MIPA-MCAS algorithms \\ \hline
13 & [59] & A 6G IoT confidence information coverage reinforcement learning node sleep scheduling algorithm and Q-learning collaborative intelligence can extend network lifetime, balance the energy consumption, and enhance resource efficiency with fewer active nodes \\ \hline
14 & [60] & A hybrid whale spotted hyena optimization (HWSHO) algorithm for 6G-enabled massive IoT device green communication in terms of energy-efficient network cluster-based data dissemination \\ \hline
15 & [75] & Deep reinforcement learning MIMO-NOMA IoT systems and Age of Information can shape optimal channel capacity, power allocation, spectrum efficiency, and energy consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of experimental data and results.