AI-Enhanced Localization Protocol for Isotropic Wireless Networks

Abstract

In the evolving landscape of wireless networks, accurate and efficient localization remains a critical challenge, particularly in isotropic environments where traditional methods often fall short. This study introduces an advanced AI-based localization protocol designed to enhance performance across various network scenarios, including low and high node densities, high mobility, and environments with interference. The proposed protocol leverages machine learning techniques to improve localization accuracy, reduce processing time, and adapt effectively to dynamic conditions.The research involves the development of the AI-based protocol, its implementation in a simulated environment, and a comprehensive performance evaluation. The protocol's effectiveness is assessed by comparing it with traditional localization methods such as triangulation and RSSI-based localization. Key metrics include localization accuracy, computational efficiency, adaptability, and scalability. The study also explores the protocol's performance under different network sizes and conditions, revealing its robustness and ability to maintain high accuracy even in challenging scenarios.Results indicate that the AI-based protocol consistently outperforms traditional methods, offering superior accuracy and faster processing times. It demonstrates strong adaptability to varying network conditions and scales efficiently with network size. The findings underscore the protocol's potential for real-world deployment in large-scale and dynamic wireless networks, providing a reliable and efficient solution for modern localization needs.This research contributes to the field by presenting a robust AI-based approach to localization, addressing existing limitations, and offering insights into its practical applications in complex network environments.