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.

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QoS-Driven Optimized Design-Based Integrated Visible Light Communication and Positioning for Indoor IoT Networks

Indoor positioning has attracted many attentions in recent years. Wi-Fi, geomagnetic and inertial sensors are commonly used sources to reconstruct pedestrian’s trajectory. But the error drift of inertial sensors and the labor-intensive fingerprints construction constrain their popularity. The purpose of this paper is to design a Wi-Fi, geomagnetic and pedestrian dead-reckoning (PDR) composed indoor positioning system which is accurate, practical and less labor-intensive. In order to achieve this goal, we design an indoor positioning system based on walking-surveyed Wi-Fi fingerprint and corner reference trajectory-geomagnetic database (CRTDB). Firstly, we propose a trajectory optimization algorithm which makes use of landmark observations to optimize historical positions of PDR by means of Gauss-Newton algorithm. Next, we construct the walking-surveyed Wi-Fi fingerprint database and the CRTDB using the optimized PDR trajectories. Secondly, we propose a Dynamic Time Warping (DTW) and Pearson Correlation Coefficient (PCC) combined CRTDB matching algorithm to find the coordinates of the observed corners. Thirdly, we propose a PDR, Wi-Fi fingerprint and CRTDB fused positioning system based on Kalman filtering (KF). Finally, we evaluate the performance and effectiveness of our proposed algorithms on two open datasets. The experimental results show that with our proposed CRTDB optimizing, the median error is improved by more than 33% and 27% respectively compared with that of PDR and Wi-Fi fusion.

Indoor Positioning Based on Walking-Surveyed Wi-Fi Fingerprint and Corner Reference Trajectory-Geomagnetic Database

This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.

FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles

Pedestrian dead reckoning (PDR) is an inertial navigation system that relies on smartphone sensors for estimating a pedestrian’s step movements. However, such systems suffer from poor accuracy due to the drift and inherent noise in sensor readings. In addition, step size variation among pedestrians and device heterogeneity pose further challenges for building a scalable PDR system that can provide uniform performance across various devices and a diverse range of users. Visible light positioning (VLP), which uses LED lights with visible light communication (VLC) capability to provide high-accuracy localization, can achieve precision of a few cm. However, VLP systems suffer from practical limitations due to occasional line-of-sight (LOS) blockage and the sparse density of lighting in large-scale indoor venues. In this work, we propose a light-assisted dead reckoning (LiDR) system, which aims to address the problems of both VLP and PDR. It uses LED lighting as high-accuracy location landmarks to provide regular calibration for the PDR and estimates the individual pedestrians’ step size for increased accuracy. In addition, a light-shape-based heading angle correction algorithm is proposed to reduce the heading angle error and further improve the accuracy. The system is implemented as an Android-based navigation application, with a digital map and cloud-based backend storage for location, device, and user-specific parameters. The real-time performance of the system is evaluated in a 450- $\text{m}^{2}$ lab and on a 150-m walking track. The experimental results demonstrate that with a maximum light spacing of 15 m, an overall average accuracy of $\lt ~0.7$ m can be achieved for the whole system.

LiDR: Visible-Light-Communication-Assisted Dead Reckoning for Accurate Indoor Localization

In this paper, we propose a novel indoor location scheme base on Frequency Modulated Continuous Wave (FMCW) radar to realize high precision positioning. Firstly, to achieve complete spatial motion information, the 3D-FFT algorithm is utilized to measure range, velocity, and angle of arrival (AOA) parameters. In particular, considering the non-linear problem in the conventional Kalman filter process, we proposed an extended Kalman filter (EKF) based tracking method to achieve high tracking accuracy. Finally, the extensive experiments demonstrate that the proposed system is able to achieve the minimum mean error of 0.6974 m, which is better than the existing schemes.

Indoor Localization and Trajectory Tracking System based on Millimeter-Wave Radar Sensor

Aiming at the problem of heavy preparation work and high cost of indoor positioning technology, this paper proposes a visible light positioning (VLP)-assisted smartphone-based pedestrian dead reckoning (PDR) indoor positioning technology. This technology combines the advantages of both PDR and VLP algorithms for indoor positioning. During the pedestrian position calculation, the method of using the averaging of mode to process the heading angle data is creatively used, which improves the positioning accuracy and increases reliable operation time. At the same time, the two-level verification method consisting of cyclic redundancy check and range constraint is implemented for VLP, which uses a LED carrying a modulation code as a beacon for absolute position and the camera of smartphone to capture/decode the LED image to obtain the absolute position and periodically correct the position of the PDR positioning, thereby avoiding the accumulation of PDR positioning technology errors. The scope and reliability of PDR are demonstrated. The experiments show that this method is a kind of low cost, high robust, and high precision indoor positioning technology.

Hongdong Zhao

Papers

Improved Smartphone-Based Indoor Pedestrian Dead Reckoning Assisted by Visible Light Positioning