###### Abstract
It also provides access to information and a high level of understanding of wireless communication systems and proposes to manage the operational aspects of more heterogeneous networks. Adding ML to wireless networks supports opening up new possibilities for implementing and improving the networks and creating a reliable, intelligent, sustainable, inclusive, and robust network that operates without human intervention [3; 4; 5; 6]. Symmetry is a widely studied pattern in various research areas. In computer science and telecommunication networks, symmetric network structures and symmetric algorithms are often studied. The study of symmetric and asymmetric faults and outliers (anomalies) in network traffic is also important. Moreover, many systems are symmetric because the data speed or volume is the same in both directions; improving QoS in the network depends on symmetry. The technology of ML can be used in various symmetric IoT applications and makes it possible that it will be an essential source of information in the future. Therefore, the networking environment can access experimental symmetric data through different network devices, explore the current data, extract information, and make conscious decisions based on the available data. ML is an essential platform for realizing intelligent IoT applications. Wide-ranging, intelligent, ML-based IoT systems will be more efficient and effective if they exploit the properties of \"symmetry\" and \"asymmetry.\" This will be helpful in various IoT applications [7; 8; 9; 10], for example:  
* Network monitoring improves security and efficiency, where network monitoring that identifies and fixes problems in applications is much more valuable to an organization to prevent unwanted system failures. * Detect intrusions, errors, and anomalies to monitor raw data, such as user records, devices, networks, and servers. Quickly detect attacks and unclear security risks. * Improving QoS requirements can be achieved by monitoring the control and management of network resources, reducing interference such as packet loss, latency, and jitter in the network. Network resources are managed through QoS by providing advantages for particular classes of data on the network. Prediction of quality Characteristics such as throughput, resource allocation, or timing issues. Model enabled accuracy prediction is a kind of prediction that applies a model as input for the prediction. Enterprise networks should provide expected and measured services for video conferencing that use real-time audio and video communications. Both are delay-sensitive and bandwidth-intensive forms of communication. Enterprises use QoS to efficiently manage sensitive applications, such as real-time voice, video, and critical data, and to avoid degradation of QoS parameters. Enterprises can achieve QoS by using certain features, such as jitter buffers and bandwidth management. For many enterprises, QoS is part of SLA with their NSP to ensure a specific stage of network performance. * Health applications. Recently, medical IoT systems have become one of the most essential modern medical advancements. This technique can reach a critical benefit by improving the distance control of healthcare. It can also support detecting medical problems rapidly, therefore protecting patients' lives and health. Nevertheless, many networked medical devices in the IoT healthcare space have security flaws that make them susceptible to malicious threats. The above challenges can cause serious consequences that influence patients' lives by disrupting medical equipment. Therefore, it is necessary to overcome these challenges to maintain the efficiency and accuracy of medical IoT systems. At the same time, the wide distribution of sensitive medical information in IoT healthcare systems leaves them vulnerable to complex attacks that aim at main security aspects such as privacy and safety. This will harm the reliability, acquisition, and widespread use of IoT healthcare systems.