AI-Driven Hybrid Batch Authentication for UAV-Assisted Mobile IoT Networks

Abstract

The rapid evolution of the Internet of Things (IoT) has led to a dramatic increase in the number of connected devices. Consequently, it is no longer feasible to deliver high-quality services using terrestrial infrastructure alone. Unmanned aerial vehicles (UAVs) are increasingly being used to improve the capacity and efficiency of IoT networks due to their maneuverability, economy, and flexibility. In resource-constrained IoT environments, UAVs typically employ batch authentication to securely and efficiently manage simultaneous access requests. However, if even a single signature is invalid, batch authentication will fail, which compromises the system’s availability and reliability. This paper introduces a hybrid dynamic access control architecture that combines batch verification techniques with machine learning (ML) algorithms to detect fraud (i.e., identify invalid signatures) before the batch verification process in order to enhance its efficiency. The experiment’s findings show that using artificial intelligence (AI) before the batch verification process can improve its efficiency and reduces the computational load.