## 4 Congestion Mitigation Algorithms for WSNs
### Previous Work on WSN Congestion Mitigation Schemes
#### 4.1.4 AI-Based Algorithms for Congestion Mitigation in WSN  
In [78], to deal with the extremely dense congestion in IoT networks, [PERSON] created a novel model-free and adaptive fuzzy neural network (NN) based on a deep reinforcement learning mechanism. These techniques utilize the MPTCP protocol, the multi-path transport control protocol. To deal with states with highly dynamic IoT traffic dimensions, this unique model is proposed that approximates the value of state action function and actor function (action). In addition, this model dynamically modifies the congestion subflow window size to account for the changing environmental conditions. The simulation results show that the proposed approach outperforms the Deep Quantum Learning (DQN) and Software Defined Network (SDN)-based MPTCP in terms of good performance. The proposed approach requires time-consuming training and execution, which leads to delays in the IoT network. A recent decoupled learning optimization procedure was presented by [PERSON] in [79]. This scheme involves the optimization of several features, including average access delay, successful device access, and average energy consumption. In addition, this algorithm jointly optimizes other RACH techniques, such as DQ, BO, and Access Class Barring (ACB). This tactic separates traffic configuration from spoilage. The Gated Recurrent Unite Recurrent Neural Network (GRU-RNN) model was previously used to predict real-time values of IoT real-time traffic. Due to the random nature of IoT real-time traffic, these values exhibit short-term correlation. The task of the target controller is to use various Deep Reinforcement Learning (DRL) agents to configure the system parameters for the RACH scheme. In addition, the goal of DQN is to manage the unique action selection of BO and the Distributed Queuing (DQ) scheme. Deterministic Policy Gradient (DDPG) is used to regulate the ongoing action selection of ACB mechanisms. The numerical results of the proposed system show that the cooperative model and the decoupled learning scheme outperform traditional learning schemes in terms of training efficiency. Moreover, cooperative learning can successfully adapt the RACH schemes, resulting in the highest performance for each RACH scheme. This shows how the training effectiveness and convergence can be improved by using projected IoT traffic data in conjunction with a learning procedure. These techniques can also be used in the 5G New Radio Network (NR) to optimize RACH schemes and can be extended to optimize solutions to dynamic challenges. A congestion avoidance strategy for Smart Grid Neighborhood Area Networks (SG-NANs) based on ML techniques was proposed by [PERSON] and [PERSON] in [80]. In this approach, the source node decides whether to transmit data based on the current traffic in the network. This decision is made based on the packets currently in the buffer and the channel utilization factor. All broadcasts and network nodes are used to measure the parameters. Additionally, based on the usefulness of data applications, the division of traffic into different categories is considered. To train the model, the approaches from ML require data sets. The proper techniques to generate data sets are the first contribution of this work. Using this data, we can better define the network behavior and the influence of the different categories. It also helps in separating the traffic categories. So, this separation leads to improved performance at the expense of the original complexity of the system. On the other hand, sometimes, feature selection is carried out to reduce the complexity. An evaluation and use of the two-classification algorithm is made. One has a low computational cost and is based on decision trees. To evaluate its performance in the first phase, the receiver operating characteristic curve (ROC) is used, which has the highest accuracy value. Moreover, at the level of the tested SG NAN settings, better improvements were obtained in terms of network delay, throughput, and PDR.