Efficient IoT indoor monitoring via distributed deep learning in hybrid VLC/RF network architectures

Abstract

The rapid growth of the Internet of Things (IoT) has driven the demand for efficient indoor monitoring systems that can provide reliable connectivity, low latency, and high energy efficiency. Traditional wireless approaches based solely on radio frequency (RF) suffer from spectrum congestion and interference, while visible light communication (VLC) systems are constrained by line-of-sight requirements and limited coverage. To address these challenges, this paper proposes a hybrid VLC/RF network architecture integrated with distributed deep learning for IoT-based indoor monitoring. In the proposed framework, IoT devices leverage VLC links for high-speed data transmission in favorable conditions and seamlessly switch to RF links to maintain connectivity in non-line-of-sight scenarios. A distributed deep learning framework is deployed across edge nodes to enable scalable and privacy-preserving analytics, reducing reliance on centralized processing while improving adaptability to dynamic indoor environments. Experimental evaluation demonstrates that the proposed system achieves higher monitoring accuracy, reduced latency, and improved energy efficiency compared to single-mode VLC or RF systems. These findings highlight the potential of hybrid communication networks combined with distributed intelligence to enhance the performance and robustness of IoT indoor monitoring applications in smart homes, healthcare, and industrial environments.