Designing Deep Reinforcement Learning enhanced edge-terminal collaborative AIoT for Intelligent Visitor Management System

Intelligent Visitor Management System (IVMS) is crucial for enhancing security and operational efficiency in smart factories and intelligent office buildings. Leveraging AIoT-driven image analysis will facilitate real-time visitor authentication and access control. However, the growing volume of interactions and the limited processing power of local terminals complicate the delivery of timely and accurate image analysis. To address these challenges, we propose an edge-terminal collaborative AIoT framework for real-time visitor management. The framework solves the limitations of traditional approaches, where local terminals are unable to handle the computational load and edge solutions experience high latency due to transmission delays. Specifically, it integrates three key components to improve system performance: a local analysis module for initial processing, an image communication module for efficient data transmission, and an edge analysis module for advanced processing. Moreover, the framework jointly optimizes image task offloading, wireless channel allocation, and image compression, all formulated as an optimization problem to ensure fast and accurate analysis. Additionally, a novel multi-level Deep Reinforcement Learning (DRL) method is further designed to dynamically refine the selection of compression and offloading strategies. By learning in real-time, the DRL model adapts to network variations, addressing the scalability and adaptability limitations of existing methods. Simulation results show that our proposed edge-terminal collaborative AIoT framework significantly outperforms both edge-only and terminal-only methods in terms of latency and accuracy.