Wireless Sensor Networks (WSNs) are widely used in applications such as environmental monitoring, healthcare, and security. However, sensor nodes suffer from limited energy, bandwidth, and processing capabilities, leading to congestion, packet loss, and reduced network lifetime. This paper reviews recent energy-efficient and congestion control techniques in WSNs, including machine learning-based routing, clustering, and optimization approaches. The study identifies limitations such as lack of adaptability, high computational complexity, and dependence on static configurations. It highlights the need for scalable, adaptive, and energy-efficient traffic management frameworks to improve performance in dynamic WSN environments.

This survey reviewed energy-efficient and congestion-aware schemes in WSNs, highlighting key strengths and weaknesses of machine learning, optimization, and clustering-based approaches. Although these methods enhance lifetime and reliability, they often lack adaptability and computational feasibility for real-world large-scale WSNs. Future research should focus on hybrid models combining deep learning with adaptive clustering and lightweight optimization to achieve sustainable, real-time congestion control in dynamic environments.

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