Role of Artificial Intelligence in Secure and Energy-Efficient Wireless Sensor Networks: A Review

Abstract

Wireless Sensor Networks (WSNs) are widely used in smart agriculture, healthcare, industrial automation, environmental monitoring, smart cities, and Internet of Things applications. However, their performance is limited mainly by restricted battery capacity and vulnerability to security attacks. Traditional security and routing methods often treat these problems separately, even though energy consumption and security are closely interconnected. Artificial Intelligence provides adaptive techniques for identifying malicious nodes, optimizing routing decisions, selecting energy-efficient cluster heads, predicting network conditions, and improving overall reliability. This review examines the role of Machine Learning, Deep Learning, Reinforcement Learning, and related AI techniques in improving WSN security and energy efficiency. It also discusses the major limitations of AI-based approaches, including computational complexity, additional energy consumption, limited training data, and practical deployment challenges. The study concludes that lightweight and adaptive AI techniques can contribute significantly to secure and energy-efficient WSNs, provided that their computational and communication overhead is carefully controlled.