## 2 Related Work
However, this approach may encounter scalability issuesas network size increases, which could challenge real-time responsiveness in massive-scale deployments. Narawade and Kolekar [21] proposed an Adaptive Cuckoo Search Rate Optimization (ACSRO) scheme for congestion control in WSNs. The ACSRO uses an adaptive cuckoo search algorithm to adjust the sending rate of sensor nodes, aiming to keep the packet arrival rate within the service rate of the parent node, thereby minimizing congestion. This method defines optimal rate adjustments based on metrics such as throughput, delay, packet loss, queue size, and congestion levels. The key advantage of ACSRO is its ability to efficiently control congestion by dynamically adjusting rate settings, which optimizes network performance even under high traffic loads. However, a potential downside is the computational complexity associated with adaptive cuckoo search, which may increase energy consumption in resource-limited WSN environments. Kalaikumar and Baburaj [22] introduced a fuzzy-enabled cross-layer protocol, Fuzzy Cross-Layer Optimization with Oppositional Artificial Bee Colony (FCOABC), to manage congestion in WSNs. The FCOABC protocol integrates Fuzzy Logic (FL) and Oppositional Artificial Bee Colony (OABC) optimization to achieve energy-efficient and reliable routing. It selects CHs by assessing factors such as Link Superiority Pointer (LSP), node density, and residual energy, aiming to address the \"hot spots\" issue that affects CHs located closer to the Master Station (MS). The clusters are organized in non-uniform sizes, with smaller clusters near the MS to reduce intra-cluster congestion, while larger clusters farther away relay data effectively. This design enhances network longevity and efficiency by using OABC for multi-hop inter-cluster routing, promoting balanced energy consumption and reliable data transmission. An advantage of this approach is its ability to maintain network stability by reducing energy use and congestion at the CH level, significantly extending network lifespan. However, a potential limitation is the protocol's complexity, which may increase computational overhead, impacting scalability in large or resource-limited networks due to the iterative FL-based CH selection process and inter-cluster routing. Antoniou, Pitsillides [23] propose a congestion control protocol for WSNs that utilizes bird flocking behavior to manage network congestion. The Flock-based Congestion Control (Flock-CC) protocol treats packets as birds that flock toward the sink node while avoiding congested regions by mimicking repulsion and attraction forces between packets, as well as an artificial magnetic field guiding packets to the sink. This approach, inspired by swarm intelligence, is simple to implement and requires minimal information exchange, leveraging decentralized control with emergent self-organizing properties. Advantages of Flock-CC include effective load balancing, high resilience to node failures, and scalability across various network sizes, outperforming traditional congestion control methods. However, the protocol's reliance on packet clustering and field-based guidance may introduce delays in high-traffic scenarios and limit its applicability in environments with rapid topology changes. Alipio and Bures [24] proposed an adaptive cache-aware congestion control mechanism called Deep Reinforcement Learning Cache-Aware Congestion Control (DRL-CaCC), specifically for WSNs using Deep Reinforcement Learning (DRL). Their method combines the Rapid Start algorithm and the DRL algorithm to dynamically adjust the Congestion Window (CWND), aiming to maximize intermediate cache utilization and reduce network congestion. The DRL-CaCC approach achieved improvements between 20-40% in terms of data throughput and reduced packet loss, outperforming baseline protocols such as Real-Time Cache-Aware Congestion Control (RT-CaCC) and other congestion control mechanisms by effectively handling high levels of packet loss and optimizing cache usage dynamically.