## 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 authors used the Bellman equation for the Q-learning process in the proposed solution. Moreover, this approach allows IoT devices to gradually learn the exclusive RA slots for data transmission. As a result, there are fewer concurrent transmissions in the WSN access network. The autonomous Q-learning process is different from this approach. It uses a collaborative method. Moreover, the congestion level of the learning process is used for RA slots. In terms of throughput, convergence time, and collision probability, the numerical results show that collaborative Q-learning has better performance. The previous congestion mitigation efforts consist of two parts. 1. Congestion mitigation based on RPL rare work is conducted with this type of methodology. 2. Congestion mitigation based on non-RPL methodologies did not consider the stack protocol of 6 LoWPAN. One rare research work has been conducted on congestion mitigation mechanisms in RPL networks. We will explain these mechanisms in the following paragraphs. The Dynamic Alternative Path Selection Scheme (DAIPaS), described in [46], is a phase-based resource regulation mechanism based on congestion mitigation without RPL. The first stage of congestion mitigation deals with light congestion, while the second stage deals with heavy congestion. The load balancing technique is used as the primary application, which controls the topology technique to prevent overloads in intermediate nodes. If the aforementioned method is unsuccessful, a hard stage is used to control the data flow and determine alternative routing paths. To reduce congestion, two methods, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO), were presented in [84]. To determine the ideal data rate of PSOGSA for each child, a multi-objective search is used with the above technique. The optimization function considers both the energy parameters of the nodes and the priority of the transmitted data packets. In [85], the authors proposed a novel solution for congestion mitigation based on packet-priority intimation (PPI). In this method, the PPI part in the packet is used to indicate its priority. This method exploits the routing protocol of the Ad hoc On-Demand Distance Vector (AODV), making it a congestion-aware mechanism. In [86], several situations of congestion in the WSN communication channel were presented. The evaluation is based on the transmitter output power level, time intervals of transmission, and generation rate of the packet. [PERSON] and [PERSON] in [24] investigated a wide range of natural AI mitigation solutions for deregulated power systems (DPSs). In addition to providing concepts and pseudocodes for optimization algorithms, such as Grey Wolf Optimization (GWO), Teaching Learning-Based Optimization (TLBO), JAYA Algorithm (JAYA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), inspired by natural processes, the authors also studied conventional and non-conventional algorithms. The authors also investigated methods of system-wide congestion mitigation. This research was conducted by the authors in the context of reducing DPS traffic congestion caused by renewable energy sources. Potentially, these methods could be used to optimize wireless sensor networks (WSNs) for effective spectrum, energy, and WSN resource management. These algorithms should be used for resource management on the tower side. The following section of the review focuses on recently developed AI-based WSN congestion mitigation techniques. Before proceeding, it is important to understand the limitations that AI algorithms face.