## 2 Related Work
### Comparative Outlook  
In IoT networks, managing congestion is crucial due to the high packet transmission rates and limited processing capabilities, especially in nodes near the sink that handle substantial upstream traffic. Congestion leads to increased energy consumption and a decline in QoS, affecting application performance and network longevity. Numerous protocols have been developed to address these challenges, each focusing on different aspects of congestion control, such as detection, notification, and mitigation. While these protocols have advanced congestion management in WSNs, limitations remain in balancing energy efficiency, network scalability, and adaptability under varying traffic loads. Table 1 presents the advantages and disadvantages of the other proposed methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Proposed Method** & **Advantages** & **Disadvantages** \\ \hline
**Rida**[17] & \begin{tabular}{c} Event-to-sink Reliable \\ Transport (ESRT) \\ \end{tabular} & \
\begin{tabular}{c} High reliability and \\ availability in data transfer \\ \end{tabular} &
\begin{tabular}{c} Limited in load balancing and \\ energy efficiency \\ \end{tabular} \\ \hline
**Aimtongkham,** & \begin{tabular}{c} Fuzzy Logic System \\ (FLS)-based RPL for IoT \\ \end{tabular} & \
\begin{tabular}{c} Mitigates congestion, reduces \\ packet loss, optimizes \\ energy usage \\ \end{tabular} &
\begin{tabular}{c} Increases computational \\ demands on IoT devices, \\ impacting network lifespan in \\ congested environments \\ \end{tabular} \\ \hline
**Bhat,** & \begin{tabular}{c} Network and \\ Application-Aware Adaptive \\ Congestion Control (NACC) \\ \end{tabular} & \
\begin{tabular}{c} Enhances adaptability and \\ network efficiency; high \\ throughput and performance \\ in multi-flow tests \\ \end{tabular} &
\begin{tabular}{c} High overhead from continuous \\ telemetry and application \\ integration, limiting scalability \\ in larger networks \\ \end{tabular} \\ \hline
**Andrade-** & \begin{tabular}{c} RL and DQN for UDP \\ congestion control in \\ smart grids \\ \end{tabular} & \
\begin{tabular}{c} Improved packet delivery \\ ratio, network throughput, \\ and latency management \\ \end{tabular} &
\begin{tabular}{c} Scalability issues with increased \\ network size, challenging \\ real-time responsiveness in \\ large deployments \\ \end{tabular} \\ \hline
**Naraade and** & \begin{tabular}{c} Adaptive Cuckoo Search \\ Rate Optimization (ACSRO) \\ \end{tabular} & \
\begin{tabular}{c} Efficient congestion control \\ through dynamic \\ rate adjustments \\ \end{tabular} &
\begin{tabular}{c} Computational complexity \\ increases energy consumption \\ in resource-limited \\ WSN environments \\ \end{tabular} \\ \hline
**Kalaikumar and** & \begin{tabular}{c} Fuzzy Cross-Layer \\ Optimization with \\ OABC (FCOABC) \\ \end{tabular} & \
\begin{tabular}{c} Maintains network stability, \\ extends network lifespan by \\ reducing CH congestion \\ \end{tabular} &
\begin{tabular}{c} Protocol complexity impacts \\ scalability in large or \\ resource-limited networks due \\ to iterative processes \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Outlines the pros and cons of the proposed methods. Many existing methods demonstrate effective congestion control but often struggle with computational demands or limited scalability in resource-constrained environments. For instance, the ESRT algorithm emphasizes reliable data transfer yet falls short in load balancing, while the FL and RL-based models introduce adaptability but may increase energy use and delay in high-traffic scenarios. Recent developments using swarm intelligence, adaptive rate control, and deep learning have improved packet delivery and load distribution but often encounter challenges in real-time response or scalability in large-scale IoT networks. These gaps indicate a need for a congestion control method that combines efficient resource use, real-time adaptability, and minimal computational overhead.