Research papers in congestion control mechanisms for the Constrained Application Protocol (CoAP) mainly address the challenge of ensuring reliable and efficient communication in resource-constrained IoT environments, where limited bandwidth, buffer size, and energy make networks highly susceptible to congestion. Since CoAP operates over UDP and is widely used for machine-to-machine (M2M) communication in Low-Power and Lossy Networks (LLNs), congestion can lead to excessive packet loss, increased latency, unfair resource usage, and energy depletion. To mitigate this, researchers have explored a variety of congestion control approaches, including adaptive retransmission timers, dynamic window-based flow control, rate limiting, cross-layer optimization, and priority-aware packet scheduling. Many studies enhance the default CoAP congestion control (based on binary exponential backoff) by incorporating round-trip time (RTT) estimation, queue length monitoring, or channel condition awareness to achieve more precise retransmission control. Others propose lightweight congestion avoidance schemes that balance reliability and energy efficiency, such as adjusting message transmission rates according to network load or prioritizing critical data in emergency scenarios. Some works leverage machine learning and reinforcement learning to predict congestion patterns and optimize sending behavior, while others integrate CoAP with Software-Defined Networking (SDN) to achieve global traffic regulation. Hybrid solutions combining CoAP with TCP-friendly mechanisms or proxy-assisted congestion control have also been reported to improve performance in heterogeneous IoT deployments. Despite these advancements, open issues remain, such as maintaining fairness among nodes, handling burst traffic, and designing mechanisms that remain lightweight enough for constrained IoT devices. Overall, the literature highlights that effective congestion control in CoAP is vital for scalable, reliable, and energy-efficient IoT communication, making it an active and evolving research area.