## 7 Merging Energy and IoT in 5G, 6G
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**ML** & **5G, 6G-Based Applications Energy and IoT** \\ \hline ANN & Their primary advantages have been identified, and the feasibility of their implementation in tackling the problem of accurate attack detection in m2m adhoc self-organizing networks has been assessed [239]. \\ \hline DL & looked into the challenges involved in trying to blend MEC with C-RAN, specifically regarding cloud resources. energy-reduction [240, 241]. \\ \hline DL & Implemented a modern vehicle-internet infrastructure-aware ML architecture. It has made managing network slicing difficult in the context of 5G-enabled networks [242]. \\ \hline DL & Has provided an APP-SON system which optimises 4G/5G network performance and user QoE. App-son offers a scalable big data platform for targeted optimization by evaluating cell application features in temporal space [243, 244]. \\ \hline DL & proposed to apply DL algorithm based on 5G-V2X for AI-based 5G base station allocation for platooning cars [245, 246]. \\ \hline RL & Found an E2E network slicing system that uses deep reinforcement learning to manage many resources to efficiently generate network slices [247, 248, 249]. \\ \hline Bayesian & Bayesian learning was used to identify the broadcast signals for LA-CDMA uplink access. Using the sparse signals, this proposed strategy leverages our ignorance about user activities. Furthermore, introduce Gaussian mixture model approach to calculate the transmitted signals [250]. \\ \hline ML & Proposed an online learning detection approach for the NOMA uplink. Build an online adaptive filter in the sum space of linear and Gaussian reproducing kernel Hilbert spaces. So, this approach is robust to dynamic wireless networks that can degrade the efficacy of a nonlinear adaptive filter [251]. \\ \hline ML & Proposed the first online learning method designed to aid beam selection in mmWave vehicular systems. in particular, see this as a multi-armed bandit problem. Next, developed a lightweight context-aware online learning algorithm, known as FML, with proven performance and assured convergence [252, 253]. \\ \hline ML & Investigated healthcare scenarios where communication is conducted at THz frequencies. Combined with an ML mechanism, THz communications protocols result in fewer signal route losses in the system [254, 255, 256]. \\ \hline ML & The new algorithm that incorporated a change in the hidden layer of the RNN is known as GRUs. The RNN-GRU model is used for determining whether or not to enable the VC mode. Received signal strength measurements were utilised to train the RNN-GRU model [257, 258]. \\ \hline ML & Most notable similarities between the cloud-fog node architecture and the human brain-spinal cord-nerve network model involve the presence of fog nodes [259, 260, 261]. \\ \hline RL & The user association problem is tackled with a reinforcement learning method that considers content placement profiles and frontal constraints [262, 263, 264]. \\ \hline ML & Detected the mood of patients by implementing an intelligent real-sense camera system prototype. ML, an SVM, and the RealSense facial detection system can be utilised to track patient demeanour for pain monitoring [265]. \\ \hline ML & Developed a lightweight context-aware online learning method called FML with performance and convergence guaranteed. Using coarse location information and aggregating the data, FML learns from and adapts to its environment. Furthermore, propose a standard-compliant protocol that utilises the existing cellular network architecture and the future 5G characteristics [266, 267, 268]. \\ \hline ML & Focused on designing and detecting RA preamble for 6G IoT satellite-assisted 6G an energy-conscious course for big IoT devices [269]. The drone follows the shortest path across a connected graph. This path decides the visiting order of devices. According to system identification theory, and with the use of neural networks, the model is constructed based on the results [270, 271]. \\ \hline NN & FA neural network combined with system identification theory is used, and the model is created based on the gathered data [272]. \\ \hline ML & Enriched standard HetNets with a user-centric ML dimension [273]. \\ \hline \hline \end{tabular}
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