Ant Colony Optimization (ACO) for congestion control in CoAP (Constrained Application Protocol) networks is a specialized research area focused on enhancing reliability, throughput, and energy efficiency in IoT environments with constrained resources. Research papers in this domain explore the application of bio-inspired ACO algorithms to dynamically adjust transmission rates, route selection, and resource allocation in CoAP-based IoT networks, thereby minimizing packet loss, latency, and network congestion. Key contributions include energy-aware routing, adaptive load balancing, hybrid ACO frameworks integrated with machine learning for predictive congestion management, and QoS-aware mechanisms suitable for heterogeneous IoT devices. Recent studies also address challenges such as scalability, dynamic network topologies, real-time responsiveness, and limited computational and energy resources of IoT nodes. By leveraging ACO for congestion control, research in this area aims to enable intelligent, efficient, and resilient CoAP-based IoT communication systems.