FL-MD3QN-Based IoT Intelligent Access Algorithm for Smart Construction Sites

With the deployment of fifth-generation (5G) mobile communication technology and rapid advancements in artificial intelligence and edge computing, smart construction sites have emerged as a critical direction for the construction industry’s transformation and upgrading. However, existing intelligent Internet of Things (IoT) access algorithms often struggle to simultaneously meet practical requirements for high-efficiency data transmission rates, low latency, and secure privacy-aware access in the dynamic and complex environments of smart construction sites. To address this, this paper proposes a federated learning-based Multi-Objective Dueling Double Deep Q-Network (FL-MD3QN)-based IoT access algorithm for multi-site, multi-modal, multi-user IoT systems under the same Base Station (BS). First, a three-objective optimization mathematical model was established. The optimization goals include maximizing data transmission rates, minimizing transmission delays, and maximizing reliability. Constraints such as bandwidth, rate, bit error rate (BER), and security/privacy are defined. Second, the FL-MD3QN algorithm is developed to solve this optimization problem. This algorithm can adaptively adjust the access strategy to cope with the complex and ever-changing communication needs of smart construction sites and, by introducing a federated learning mechanism, it achieves collaborative optimization of multiple construction site IoT systems while ensuring user privacy. Simulation results demonstrated significant advantages of the FL-MD3QN algorithm. For latency, it achieved markedly lower delays across multi-modal services compared to benchmark algorithms, with the shortest training time. In transmission rates, FL-MD3QN delivered the highest average rates, particularly excelling in video services. Under high signal-to-noise ratio conditions, FL-MD3QN achieved exceptionally low BER values. Additionally, it attained high levels in average access success rate and average reward value, confirming its robust adaptability and optimization performance in complex smart construction environments.