A Comprehensive Survey on Energy-Efficient Wireless Sensor Network Protocols for Real-Time Applications

Wireless Sensor Networks (WSNs) play a critical role in real-time applications, such as healthcare monitoring, industrial automation, and smart cities. However, energy efficiency remains a significant challenge due to the limited power resources of sensor nodes. This paper presents an AI-based energy-efficient routing protocol leveraging Machine Learning (ML) techniques to optimize cluster head selection, routing paths, and data transmission. The proposed method integrates K-Means clustering, reinforcement learning-based routing, and AI-driven anomaly detection to enhance network lifetime, minimize energy consumption, and improve security.