Enhancement of Wireless Sensor Networks (WSN) Using Artificial Intelligence (AI) and Internet of Things (IoT): A Review

Wireless Sensor Networks (WSN) have emerged as foundational infrastructure for modern intelligent systems, enabling real-time environmental monitoring, smart city management, precision agriculture, and industrial automation. However, traditional WSN architectures face persistent challenges including energy inefficiency, limited scalability, constrained bandwidth, and vulnerability to security threats. The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) with WSN presents transformative opportunities to overcome these limitations. This review article systematically examines recent advancements in the integration of AI-driven techniques—including machine learning, deep learning, and reinforcement learning—with IoT-enabled WSN platforms. The paper reviews routing optimization, anomaly detection, energy harvesting, edge intelligence, and security enhancement strategies. Furthermore, the review discusses emerging application domains spanning smart agriculture, smart cities, consumer electronics, and industrial systems. Ethical, privacy, and regulatory considerations are also critically analyzed. The findings indicate that AI-IoT-WSN convergence significantly improves network lifetime, data reliability, and decision-making accuracy, while simultaneously raising important questions around data governance and algorithmic fairness. Future research directions and open challenges are outlined to guide the next generation of intelligent sensor network design.