## 1 Introduction  
The Internet of Things (IoT) has improved quickly, leading to smart sensors that work on their own [1]. These sensors can automatically collect and share data and are often spread out in different places to track things like humidity, movement, heat, smoke, or pressure. They can be networked and used in hard-to-reach places to ensure reliable communication [2]. Typically, data collected by these sensors are sent to a Base Station (BS) through a Cluster Head (CH) or other nodes [3]. Connecting such devices to the Internet allows users to analyze the data, offering appealing insights and automation options [4]. IoT connects the physical and digital worlds, making automation possible across various fields [5]. These sensors collect massive amounts of data, help improve insights or organize actions in industries like environmental monitoring, healthcare, and air pollution detection. IoT devices communicate with one another using Internet protocol (IP) [6], the same system that links computers on the Internet [7]. Congestion in data forwarding occurs when too much data tries to pass through a network, overwhelming one or more points along the path [8]. This slows down data transfer and can lead to data loss [9]. Unmanaged data forwarding in an IoT network can lead to congestion, overwhelming the network's capacity [10; 11]. This results in slower performance, delays, and potential data loss. It is helpful to measure congestion under specific conditions. Modern congestion management protocols, tailored to specific application requirements, provide more effective solutions than traditional methods [12]. Effective congestion control adjusts data rates at the source to reduce or avoid congestion. Artificial Intelligence (AI) algorithms play a role by helping sensor nodes share resources fairly, especially when bandwidth is limited [13]. One major benefit of controlling congestion in IoT is improving Quality of Service (QoS), ensuring data can move successfully from start to end [14]. This paper introduces an eight-step approach to improve congestion control in Wireless Sensor Networks (WSNs). The goal is to detect, notify, and manage congestion effectively while keeping energy usage low and data transmission reliable. It starts with setting up the network, where nodes are spread evenly across a defined area, each with a limited amount of initial energy. Direct communication with the BS is optimized by calculating energy based on distance, reducing power usage. Next, cluster generation is conducted using Generative Adversarial Networks (GANs) [15], where clusters are synthesized and validated through a discriminator until the generated and real clusters are indistinguishable, promoting realistic grouping. CHs are selected using Ant Colony Optimization (ACO) [16] based on energy, distance, and centrality within clusters, improving communication efficiency. Pheromone-based routing is then employed to identify optimal paths, with initial pheromone levels encouraging shorter routes and further updates reinforcing reliable paths. Congestion is detected using a congestion factor (_CF_), calculated as the ratio of data flow to link capacity, triggering alternate routing if thresholds are surpassed. Key contributions of this paper include:  
* **Congestion Control Framework:** A multi-stage approach for detecting, notifying, and mitigating congestion in WSNs, addressing energy and traffic challenges. * **Energy-Efficient Routing:** Utilizes GANs for realistic clustering and ACO for efficient CH selection to balance network load. * **Pheromone-Based Routing:** Introduces a pheromone system for selecting low-congestion paths, improving data flow and reliability. * **Integrated QoS Metrics:** Uses key QoS metrics (latency, throughput, reliability) to evaluate and enhance network performance under congestion.