Comparative Analysis of Neural Network-Based Routing Algorithms for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are integral to diverse applications, including environmental monitoring, healthcare, and smart city infrastructure. The efficiency of data routing within these networks is crucial for optimizing performance and extending the lifespan of sensor nodes. This paper presents a comparative study between neural network-based routing algorithms and traditional protocols in WSNs. The analysis focuses on key performance metrics such as energy consumption, packet delivery ratio, and end-to-end delay across various network scenarios. Our findings highlight the advantages of employing neural networks, which can adapt to dynamic network conditions, thereby enhancing overall efficiency. The study emphasizes the potential of integrating machine learning techniques into WSN routing protocols, offering more intelligent, responsive, and energy-efficient network management solutions. The results suggest a promising future for machine learning-driven approaches in WSNs, with future research opportunities including the exploration of advanced machine learning models and their applicability in large-scale and heterogeneous WSN deployments. This work contributes to the evolving landscape of WSNs, aiming to support the development of smarter and more sustainable networks.