## 4 Congestion Mitigation Algorithms for WSNs
### Previous Work on WSN Congestion Mitigation Schemes
#### 4.1.1 Conventional Algorithms for Congestion Mitigation in WSN
Moreover, it also facilitates Software Defined  
Routers (SDRs) go via the SDN controller for traffic load scheduling, helping to meet the better usage of network resources as well as the mitigation of congestion WSNs. Reinforcement learning has the ability to take hard decision capability; hence, in [35], [PERSON] and [PERSON] proposed one of the novel resource allocation mechanisms by utilizing deep reinforcement learning for optimizing the reassignment of multi-channel on controlling the traffic in SDN-IoT (core backbone network). The authors developed a multi-channel reassignment for the optimization of the objective function through a traffic control and multi-channel reassignment (TCCA) algorithm and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) called (TCCA-MADDPG). To handle the backbone network complexity of the network and dynamics, the authors employed IoT traffic prediction results, making the information part of the channel state. At the same time, the TCCA-MADDPG algorithm alleviated the environment with multi-agent instability by partly observing the state through the multiple agents' cooperation. To ensure better time utilization continuity states in the channel, a Long Short-Term Memory (LSTM) layer is added to the neural network. The simulated results reveal that the proposed mechanisms outperform the existing schemes and converge quickly. Smart cities with wireless IoT networks will include massive machine-based communications (mMTCs) over the cellular network. However, the dynamic characteristics, including quality of service requirements, transmission frequency, payload size, and energy efficiency, pose significant obstacles to the development of IoT applications based on the wireless communications of the future. The major obstacles in WSNs are congestion mitigation and effective energy management during periods of high traffic. Massive random access attempts cause severe degradation in WSN performance, as they result in a sharp increase in the probability of preamble collisions and congestion to manage the enormous access that is the root cause of many problems in the wireless IoT network. In [36] [PERSON], [PERSON] and [PERSON] proposed a unique random access-based collision resolution model for heavy IoT traffic. The basic preamble collisions are resolved by the proposed solution. The simulation results show how S-ALOHA models have affected the success rate of RA. With excellent reliability and efficiency, this model can coexist with a Long-Term Evolution-Advanced (LTE-A) Media Access Control (MAC) layer protocol and network access. Congestion on RACHs in LTE-A can result from WSNs accessing the channel simultaneously. The Extended Access Barrier (EAB) strategy is the basic choice of 3 GPP to reduce RACH congestion by blocking low-priority devices. Various EAB-based strategies are proposed to restructure the arrival of IoTs; nevertheless, these mechanisms provide erratic performance. In [21], [PERSON] et al. provided an analytical model to study the effectiveness of the EAB algorithm in LTE-A networks. Simulations show the accuracy of the analysis. The analytical model theme targeting the QoS constraint could achieve the best values for the repetition period for system information block type 14 (SIB14). To prevent congestion before it occurs, [PERSON], [PERSON], and [PERSON] developed the Novel Co-operative and Fully Distributed Congestion Control (NCFCC) strategy for CCN in [37]. CMTB is one of the hybrid congestion management techniques and combines Dynamic Token Bucket and Receiver-Based Congestion Management (FDCC), which is a modified version of additive increase or multiplicative decrease. The proposed plan operates in two phases. The second phase is initiated when the CMTB asks the FDCC to reduce traffic after trying all the middle nodes but without success. PSR messages are used to turn on the FDCC. By changing the data transmission rate of the intelligent consumer nodes, the FDCC takes control.