Integrating Edge Computing and AI for Energy-Efficient Data Processing in Wireless Sensor Network

Abstract

Wireless sensor networks are critical for applications like industrial automation, smart cities, healthcare, and environmental monitoring. However, challenges arise with energy consumption, latency, and bandwidth due to the large data generated. Integrating Edge Computing and AI offers a solution by enabling efficient data analysis with lower energy consumption. Edge computing brings processing closer to data sources, allowing real-time data analysis without relying on cloud servers, reducing energy usage and extending network lifespan. AI techniques, such as machine learning and deep learning, enhance data filtering, anomaly detection, and predictive maintenance at the edge. This approach boosts WSN scalability and responsiveness by managing large data efficiently. This study examines WSN architectures with edge computing and AI, focusing on key frameworks, technologies, and energy-saving methods. Through case studies, it demonstrates how this integration enhances performance, data efficiency, and supports intelligent decision-making in various WSN deployments.