## 4 Congestion Mitigation Algorithms for WSNs
### Previous Work on WSN Congestion Mitigation Schemes
#### 4.1.4 AI-Based Algorithms for Congestion Mitigation in WSN
The behavior of the system is tested at a higher level in different traffic categories, and QoS is observed. To clarify the results, the compliant throughput and the compliance factor are introduced at this level. In the last level, the categorization is performed using neural networks. Better simulation results were obtained for all these categories, albeit at a cost due to computational complexity. In [41], [PERSON] et al. proposed a congestion solution in IoT networks for Constrained Application Protocol (CoAP), IPv6 over Low-Power Wireless Personal Area Network (6 LoWPAN) networks. The proposed algorithm applies the swarm intelligence notion procedure for forwarding the packets of data via routes free from traffic and avoiding congested routes. In [81], [PERSON], [PERSON] proposed a congestion-aware algorithm using fuzzy logic (CAUF) for finding the routing path that will be optimal to prevent congestion from picking the best parent from the tree structure of WSN. The model of parent selection is added to a multi-attribute decision making (MADM) by using a fuzzy weighted sum model. In [44], [PERSON] proposed a congestion mitigation three-step scheme. Each step uses one algorithm to achieve the purpose. (1) Level Detection Algorithm, step one, named setup phase. (2) Request Distribution Algorithm, named request distribution phase. (3) Event Distribution Algorithm, named data routing and event occurrence report phase. In fact, in medical WSN, various sensors have different priorities. This scheme senses the various sensors with different priorities towards the gateway. Before the transmission of packets, priority is set by source nodes based on the data's importance. [PERSON] et al. [82] proposed a Fairness-Aware Congestion control scheme (FACC). In this scheme, a congestion-aware rate-based fairness control was reported. In this method, all intermediate WSN sensor nodes are divided into near-sink and near-source nodes. The congestion is detected by the loss of packets at the sink node. In [83], [PERSON] et al. proposed a Topology-Aware Resource Adaptation (TARA) solution to minimize congestion in IoT networks. The focus of this scheme is an additional resource adaptation for congestion alleviation in WSNs. This is achieved by activating a peculiar sleeping sensor node to establish the new topology. This new topology manages heavy network traffic. To reduce congestion in WSNs, [PERSON] et al. in [44] offered the Hierarchical Tree Alternative Route (HTAP) method. To find all promising paths leading to the sink, a tree is built based on the source. For the purpose of forwarding the additional packets, HTAP chooses the node with the smallest buffer. However, this plan results in a significant time delay and message overhead. [PERSON] et al. proposed a Convolutional Neural Network (CNN) in [48] to increase the bandwidth of [PERSON] et al. 's contribution by inserting additional layers. The concept of a multilayer deep learning Neural Network in an industrial WSN with a clustering-based method to optimize the transmission power was proposed by [PERSON] et al. in [50]. The application of CNN deep learning algorithms was found to improve the QoS and optimize the security aspects of WSN. Recurrent feedback increased the strength and efficiency of the CNN structure. To minimize congestion in WSNs, [PERSON] in [51] proposed an I-IoT-based modified element-wise attention gate using a Convolutional Recurrent Neural Network (EG-CRNN) deep learning algorithm. The proposed system was used to predict the number of packets in the WSN and to help manage the cluster heads of the WSN. This plan takes advantage of self-feedback, which improves both short-term and long-term memory. To update the weights of the EG-CRNN, the above approach also proposed a deep learning training algorithm. Moreover, it accelerates the process training required to achieve the error target. To reduce the overload in WSNs, the authors of [52] presented a unique approach called collaborative distributed Q-learning.