## 2 Contribution of This Work
### Why Use an Artificial Intelligence-Assisted Solution?  
The development of artificial intelligence is often considered essential to the development of engineering and technology. In the wireless communication network of the near future based on the Internet of Things, artificial intelligence will play an essential role in meeting the requirements of the communication system. Smart infrastructures, including smart grids, smart homes, smart cities, smart meters, and a globally interconnected smart grid, will proliferate in the coming era. Internet of Everything (IoE) communications technology will be used to make the concept of a smart world a reality. To realize a smart society, a wireless network based on the IoE must also be built. It is critical to make communication systems and other key components intelligent to improve their functionality. It is crucial to find an artificial intelligence (AI)-based solution to meet the future needs of a technologically advanced and environmentally friendly society [19]. The list of Nomenclature is shown in Table 1.  
_Why Use an ML-Based Congestion Mitigation Solution for WSNs?_  
A subfield of artificial intelligence (AI) called machine learning (ML) is concerned with using machine learning algorithms to help computers acquire new information and skills. These algorithms make it easier for a computer to evolve into an intelligent being. We will explore the many types of machine learning and their associated algorithms in later sections. It is predicted that advanced systems will emerge in the near future that have the ability to self-heal, be highly dynamic, and be self-preserving. In these systems, machine learning is of critical importance. Future wireless networks will be intelligent enoaugh to meet the above requirements, with DL and ML algorithms playing a crucial role [19, 20]. It is expected that resource management in the extremely dense IoT network will be significantly affected by deep learning (DL) and machine learning (ML) [21]. A comparison of the contributions made by researchers in the field of IoT network congestion mitigation is shown in Table 2. The utilization of deep neural networks (DNNs) in WSN devices facilitates the capability of these IoT devices to perform intricate sensing tasks and foster collaboration between the environment and humans [22].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Acronyms** & **Definition** \\ \hline IOT & Internet of Things \\ IP & Internet Protocol \\
6 LoWPAN & IPv6 over Low-Power Wireless Personal Area Network \\ QoS & Quality of Service \\ I-IOT & Intelligent Internet of Things \\ RACH & Random Access Channel \\ ROC & Receiver Operating characteristic \\ SGNANs & Smart Grid Neighborhood Area Networks \\ SoNCF & Self-organizing network coordination framework \\ TCP/IP & Transmission Control Protocol IP \\ TARA & Topology Aware Resource Adaptation (TARA) \\ LTE-A & Long-Term Evolution-Advanced \\ LSTM & Long Short-Term Memory \\ ML & Machine Learning \\ MZM & Machine to Machine \\ MAC & Media Access Control \\ CAT-M & Machine type category \\ mMTC & Massive Machine Type Communication \\ MADM & Multi-Attribute Decision Making \\ CAT-N & Narrowband IoT Category \\ OS & Operating system \\ OHCA & Optimization-based Hybrid Congestion Alleviation \\ PB-ALOHA & Pseudo Bayesian ALOHA \\ PRA & Prioritized Random Access \\ PSO & Particle Swarm Optimization \\ RPL & Routing Protocol low-power and lossy networks \\ SOSUS & Sound Surveillance System \\ SDN-IoT & Software-Defined Networking based on IoT \\ SDRs & Software-Defined Routers \\ TR & Technical Report \\ UDP & User Datagram Protocol \\ USA & United States of America \\ WSNs & Wireless Sensor Networks \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature in the survey.