Ai-Driven Optimization of Iot Antenna Design for Enhancing Efficiency, Adaptability, and Performance

Abstract

The Internet of Things (IoT) relies heavily on wireless communication principles, necessitating various wireless network components for its design and deployment, including antennas, antenna support systems, and edge computing facilities, gateways, and data centers. Given the small size and resource constraints of IoT nodes and sensors, these wireless components must be tailored to their specific applications and required characteristics. This research explores the integration of artificial intelligence (AI) techniques in the design and optimization of antennas for IoT applications. Leveraging machine learning algorithms, the proposed intelligent antenna system enhances performance metrics such as gain, efficiency, and radiation patterns. A comprehensive theoretical framework is presented and validated through simulation and experimental results. The study demonstrates significant improvements in antenna performance, adaptability to dynamic environments, and the potential for real-time reconfiguration. Additionally, it addresses the challenges associated with the architectures used for both large-scale and small-scale IoT deployments. These findings underscore the promising role of AI in advancing IoT antenna technologies, leading to more efficient and adaptable IoT networks.