QoS-driven optimized design-based integrated visible light communication and positioning for indoor IoT networks

• Main Authors: Yang, Helin, Zhong, Wen-De, Chen, Chen, Alphones, Arokiaswami, Du, Pengfei
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Electrical and electronic engineering; Internet of Things (IoT); Link Blockage
• Online Access: https://hdl.handle.net/10356/142888
• Institution: Nanyang Technological University

With the rapid development of the Internet of Things (IoT) in the smart city, smart grid, and smart industry, indoor communication and positioning are important for IoT. However, radio-frequency (RF)-based wireless networks may fail to guarantee different quality-of-service (QoS) requirements of devices, due to the limited bandwidth, severe interference, and multipath reflections. Hence, this article presents a new integrated visible light communication (VLC) and VLC positioning (VLCP) network for IoT to provide both high-speed communication and high-accuracy positioning services. As the network consists of multiple VLC access points (APs), we propose jointly optimizing the AP selection, bandwidth allocation, adaptive modulation, and power allocation approach to satisfy different QoS requirements of indoor devices while maximizing the network data rate. A low-complexity iterative algorithm is presented to solve the resource management (RM) optimization problem by decomposing it into two subproblems. Finally, a robust handover mechanism and a pedestrian dead reckoning (PDR)-assisted VLCP scheme are presented to maintain good performance under line-of-sight (LOS) blockages. The simulation results verify that the proposed solutions outperform other existing solutions in terms of effectively enhancing the data rate, improving the positioning accuracy, and guaranteeing devices' QoS requirements. In detail, the mean position error is reduced from 20 to 4.3 cm by using our presented integrated VLCP model. The proposed RM approach achieves a satisfied QoS level improvement of up to 20.3% compared with the non-QoS-driven RM approach, and it achieves the high data rate up to 1.31 Gb/s.