Enhancing Routing, and Data Aggregation in WSNs Using Artificial Intelligence: A Survey
DOI:
https://doi.org/10.65405/tqvjbc06Keywords:
Wireless Sensor Networks (WSNs), routing, Artificial Intelligence (AI), data aggregation.Abstract
Wireless Sensor Networks (WSNs) are widely used in many applications such as smart cities, agriculture, healthcare, and environmental monitoring. However, WSNs face several challenges, including limited energy, weak processing power, routing problems, data redundancy, and poor scalability in large networks. Artificial Intelligence (AI) has emerged as a promising solution to improve the performance and efficiency of WSNs.
This survey paper reviews how AI techniques are used to enhance routing, data aggregation, and scalability in WSNs. It discusses different AI methods such as machine learning, deep learning, reinforcement learning, and fuzzy logic, and explains how they help optimize data transmission, reduce energy consumption, and improve network lifetime. The paper also highlights real-world applications, key challenges, and future research directions in AI-based WSN systems.
This study aims to provide researchers and practitioners with a clear and simple understanding of the role of AI in improving WSN performance and supporting the development of more intelligent and efficient sensor networks.
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