## 2 Related Work  
In IoT networks, congestion mainly occurs due to two factors: a high rate of packet sending and the rate at which packets are processed. This issue often happens with sensors near the BS because they manage more data traffic. Congestion increases energy use and reduces the QoS for applications. Many congestion control protocols have been developed to manage heavy traffic in IoT, with different methods for detecting, notifying, and preventing congestion studied in detail. Ridha [17] developed the Event-to-sink Reliable Transport (ESRT) algorithm, which ensures reliable data transfer from the source to the destination while using resources efficiently. ESRT uses local congestion detection, and it centrally manages resources based on essential access needs. In this approach, nodes can access all network resources through broadcasting. Each sensor node monitors its queue size and sends this information to the destination node to report congestion status. The results show that ESRT provides high reliability and availability, though it has limitations in load balancing and energy efficiency. Aimtongkham, Musikawan [18] propose a novel fuzzy logic system (FLS)-based method to improve path selection in Routing Protocols for Low-Power and Lossy Networks (RPL) within IoT settings. Their approach improves routing efficiency by implementing three FLS-based components: a fuzzy multicriteria mechanism, a fuzzy congestion awareness mechanism, and a fuzzy load balancing mechanism. These components work together to manage traffic dynamically across multiple destination-oriented directed acyclic graphs (DoDAGs), adapting to varying congestion levels. Specifically, the proposed FLS determines path selection by adapting to the congestion levels--whether within a single DoDAG or across DoDAGs, using a multi-DoDAG node for traffic rerouting in extreme congestion. Simulation results suggest that this method effectively mitigates congestion, reduces packet loss, and optimizes energy use, thereby outperforming traditional RPL techniques. However, while the FLS approach is highly adaptive to congestion, it may increase computational demands on resource-constrained IoT devices, potentially impacting overall network lifespan in environments with persistent congestion. Bhat, Haxhibeqiri [19] proposed a network- and application-aware adaptive congestion control algorithm (NACC) to address the diverse QoS requirements of applications within private professional networks. This algorithm, designed to operate in wireless environments, integrates in-band network telemetry (INT) and application-network (APP-NET) data to adjust congestion control dynamically based on real-time network context and application priorities. The NACC method processes aggregated flow information from intermediate nodes, enabling decentralized, congestion-free service differentiation. The proposed method outperformed the CUBIC algorithm, achieving threefold throughput in single-flow tests and twice the performance in multi-flow scenarios. However, while NACC enhances adaptability and network efficiency, its reliance on continuous telemetry and application integration may introduce overheads, limiting scalability in larger, more complex network architectures. Andrade-Zambrano, Leon [20] propose a congestion control algorithm tailored for smart grid networks by combining reinforcement learning (RL) and deep Q-networks (DQN) to manage User Datagram Protocol (UDP) based data transmission. The DQN model autonomously learns and adapts to congestion patterns in real time, eliminating the need for extensive dataset labeling, which is often a drawback in supervised learning. This self-learning mechanism provides flexibility for intricate smart grid scenarios like urban deployments with high node density. The key advantage of Andrade-Zambrano et al. 's method lies in its improved packet delivery ratio (PDR), network throughput, and latency management under various network loads and urban conditions, optimizing smart grid reliability and efficiency.