## 2 Contribution of This Work  
There are many published surveys on the topic of congestion mitigation in communication networks. This is one of the hot topics in communication. However, there is little work on congestion mitigation in WSNs. Specifically, a survey that provides an in-depth discussion on conventional as well as future emerging AI solutions in the context of WSN congestion mitigation is not available. In this survey, we present a review of state-of-the-art published work in the prospect of congestion mitigation in ultra-dense WSNs with futuristic AI solutions. The contributions of this work are as follows:  
1. This survey outlines the scope of AI-based future emerging solutions for the congestion mitigation of WSNs.
2. WSN congestion mitigation solutions are divided into four major categories to support novice as well as expert readers' understanding by means of logical segregation of the state-of-the-art literature.
3. This survey also provides criteria for integrating AI solutions into WSNs.
4. Each section is comprehensively summarized with necessary figures and tables.
5. Critical analysis of various types of AI solutions along with their applications in IoT is also an integral part of this study.
6. This study highlights future recommendations for congestion mitigation in WSNs.  
Congestion is the overload of data produced by IoT devices as a result of a network failure. In this case, the rate of incoming data is greater than the transmission rate. Since the bandwidth of the channel is limited, the collision rate increases. Therefore, the increased rate of retransmission results in wasted energy, lower throughput, and shorter network lifetime [13]. Congestion can occur on both the node side and the link side.  
Node side: When the arrival rate of packets exceeds the departure rate of packets on the specific node side.  
Link side _congestion:_ Here, the reason for congestion is contestation, bit error, and collision [14]. Link-level congestion is related to the sharing of channels between numerous nodes by the Media Access Control (MAC) layer contention-based protocols [15].  
Congestion in WSNs is an important issue and affects throughput, energy efficiency, and network capacity. A detailed discussion on each is given in the following.  
1. Throughput  
System throughput increases during acute congestion in extremely dense wireless IoT networks. As a result, network resources are not optimally utilized, and the backlog of IoT devices grows. The probability of network failure also increases with the duration of this situation. As a result, congestion occurs.
2. Network Capacity  
Network capacity is one of many network parameters being severely impacted by the increasing congestion of the Internet of Everything. Network capacity is decreasing, and performance is suffering as network congestion increases. One of the main causes of performance decline is network capacity.
3. Efficiency  
Energy efficiency is one of several factors affecting the operating cost of a network, and it is of great importance. The energy efficiency of the wireless network for the Internet of Things (IoT) is reduced in situations of extreme congestion, which increases the operating costs of the network and increases carbon emissions to the atmosphere.  
1. Open Loop Congestion Mitigation  
In this congestion control system, there is no feedback, which is known as open loop control. Moreover, it requires the use of a user-side or eNB-side congestion minimization mechanism. We could also refer to the above idea as a unilateral congestion mitigationtechnique to convey it differently. By intervening on either the source or the destination side, this strategy reduces congestion at an early stage. The open loop control system is characterized by its accessibility, ease of use, and slightly reduced precision.