Real-time Indoor Positioning System Research Articles

Multiple WiFi Access Points Co-Localization Through Joint AoA Estimation

Indoor localization is a fundamental task to many real-world applications, which however remains unresolved, especially with commodity WiFi Access Points (APs). In this paper, we tackle this problem and propose an accurate, robust, and real-time indoor localization system that can be directly deployed on commodity WiFi infrastructure. Specifically, the proposed system makes three key contributions: 1) we introduce a non-parametric metric to measure the accuracy of Angle of Arrival (AoA) estimation; 2) we are the first to explicitly consider the relationship among the AoAs of different APs and propose a multiple APs co-localization algorithm to exploit such a relationship to improve the localization performance; 3) we propose several strategies to reduce the computational complexity of our system to achieve real-time localization. Extensive experiments are conducted to evaluate the performance of the proposed system under various situations, which demonstrate that the proposed system can achieve a 4 degrees median error of AoA estimation and 30 cm localization median error, outperforming the state-of-the-art systems.

Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning

Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning

A Wi-Fi-Based Passive Indoor Positioning System via Entropy-Enhanced Deployment of Wi-Fi Sniffers.

This study presents a Wi-Fi-based passive indoor positioning system (IPS) that does not require active collaboration from the user or additional interfaces on the device-under-test (DUT). To maximise the accuracy of the IPS, the optimal deployment of Wi-Fi Sniffers in the area of interest is crucial. A modified Genetic Algorithm (GA) with an entropy-enhanced objective function is proposed to optimize the deployment. These Wi-Fi Sniffers are used to scan and collect the DUT's Wi-Fi received signal strength indicators (RSSIs) as Wi-Fi fingerprints, which are then mapped to reference points (RPs) in the physical world. The positioning algorithm utilises a weighted k-nearest neighbourhood (WKNN) method. Automated data collection of RSSI on each RP is achieved using a surveying robot for the Wi-Fi 2.4 GHz and 5 GHz bands. The preliminary results show that using only 20 Wi-Fi Sniffers as features for model training, the offline positioning accuracy is 2.2 m in terms of root mean squared error (RMSE). A proof-of-concept real-time online passive IPS is implemented to show that it is possible to detect the online presence of DUTs and obtain their RSSIs as online fingerprints to estimate their position.

Real-Time Indoor Positioning and Route Guidance System by using Beacons

Real-Time Indoor Positioning and Route Guidance System by using Beacons

Real-time indoor positioning and route guidance system by using beacons

Real-time indoor positioning and route guidance system by using beacons

Corrigendum to “High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot”

Corrigendum to “High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot”

EdgeLoc: A Robust and Real-Time Localization System Toward Heterogeneous IoT Devices

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet of Things (IoT), heterogeneous smartphones and wearables have become ubiquitous. However, the ILBSs for heterogeneous IoT devices confront significant challenges, such as received signal strength (RSS) variances caused by hardware heterogeneity, multipath reflections from complex environments, and localization time restricted by computation resources. This article proposes EdgeLoc, a robust and real-time indoor localization system toward heterogeneous IoT devices to solve the above challenges. In particular, the RSS fingerprinting data of Wi-Fi is employed for localization and tackling the heterogeneity of IoT devices in twofold. First, feature-level and signal-level solutions are presented to address the random RSS variances. At the feature level, this work proposes a novel capsule neural network model to efficiently extract incremental features from RSS fingerprinting data. At the signal level, a multistep dataflow is further devised to process RSS fingerprints into image-like data, which utilizes the feature matrix to reduce absolute sensing errors introduced by hardware heterogeneity. Second, an edge-IoT framework is designed to utilize the edge server to train the deep learning model and further supports real-time localization for heterogeneous IoT devices. Extensive field experiments with over 33 600 data points are conducted to validate the effectiveness of EdgeLoc with a large-scale Wi-Fi fingerprint data set. The results show that EdgeLoc outperforms the state-of-the-art SAE-CNN method in localization accuracy by up to 14.4%, with an average error of 0.68 m and an average positioning time of 2.05 ms.

Developing a Novel Real-Time Indoor Positioning System Based on BLE Beacons and Smartphone Sensors

In this work, we study the problem of fusing one Pedestrian-Dead-Reckoning-based (PDR-based) position measurement and one instant Received-Signal-Strength-based (RSS-based) position measurement. This situation can arise in a smartphone-based indoor positioning system when we want to locate a moving user in real-time with sustainable accuracy, but the RSS sampling ability of smartphones is limited; for example, one RSS sample per second. Firstly, by investigating RSS’s heterogeneity, we offer a solution for RSS-based continuous positioning problems under a low RSS sampling rate that satisfies real-time requirements. Secondly, we propose a method to improve accuracy for the RSS-based position estimation method, i.e., multilateration using Least Square Estimation. We consider PDR-based and improved RSS-based positions both have Gaussian uncertainty due to initial position plus drifting and RSS-to-distance conversion, respectively. Then, the Kalman filter will fuse two kinds of Gaussian distribution to produce more precise positions. The method is intended to design a real-time system for locating a moving target. Experiments are conducted in real indoor space with a commodity device. Its results show that our proposed solution is highly accurate and feasible in actual deployment.

Using Real-Time Indoor Resource Positioning to Track the Progress of Tasks in Construction Sites

Lean construction methods have demonstrated potential to improve construction productivity. For example, the location-based management system and the last planner system have increased the reliability of planning and control in construction production. However, these benefits are often reduced because of inaccurate manual data collection. To alleviate these problems, technologies for automated monitoring of workers have been developed to identify site events in chaotic environments. This paper aims to investigate whether a Bluetooth low-energy-based real-time indoor positioning system can monitor task progress from workers’ presence. Our findings suggest that the proposed system is a feasible solution for monitoring task-level progress when there are explicit dependencies between tasks. This method could automatically detect task start and finish times and estimate the hours required to complete a task. This enables the measurement of waste hidden inside tasks, which allows for interventions for improving flows and eliminating waste.

Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration schemes which measures the Received Signal Strength Indication (RSSI) and the Time Difference of Arrival (TDoA) for position estimation of the target. The project is further extended to obtain a comparative analysis between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link specially for the TDoA scheme accompanied by increasing number of beacon nodes.

High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot

For mobile robots and location-based services, precise and real-time positioning is one of the most basic capability, and low-cost positioning solutions are increasingly in demand and have broad market potential. In this paper, we innovatively design a high-accuracy and real-time indoor localization system based on visible light positioning (VLP) and mobile robot. First of all, we design smart LED lamps with VLC and Bluetooth control functions for positioning. The design of LED lamps includes hardware design and Bluetooth control. Furthermore, founded on the loose coupling characteristics of ROS (Robot Operator System), we design a VLP-based robot system with VLP information transmitted by designed LED, dynamic tracking algorithm of high robustness, LED-ID recognition algorithm, and triple-light positioning algorithm. We implemented the VLP-based robot positioning system on ROS in an office equipped with the designed LED lamps, which can realize cm-level positioning accuracy of 3.231 cm and support the moving speed up to 20 km/h approximately. This paper pushes forward the development of VLP application in indoor robots, showing the great potential of VLP for indoor robot positioning.

Design of a Real-Time Indoor Positioning System Based on Visible Light Communication

Design of a Real-Time Indoor Positioning System Based on Visible Light Communication

Real-time indoor positioning system for a smart workshop using white LEDs and a phase-difference-of-arrival approach

In order to provide a cost-effective indoor positioning and tracking service for autonomous ground vehicles and other important assets in smart factories, we report the theory and experiments for a real-time indoor positioning system using commercially available LED lamps. Inspired by the fundamental theory of the global navigation satellite system, the proposed system uses the phase difference of arrival (PDOA) approach to obtain the time difference of arrival of each carrier transmitted from individual modified LED lamps so as to estimate the receiver position. A prototype of the atto-cellular positioning system covering an area of 2.2 × 1.8 m2 with a height of 2 m was designed and experimentally demonstrated. For the design, we performed a simulation based on the Cramer–Rao bound to achieve optimal LED lamp arrangement, RF power, and other parameters. Furthermore, a virtual local oscillator for the PDOA scheme was applied to reduce the hardware complexity and to ensure the processing speed. In the experiment, the receiver was mounted on a movable material buffer station in a smart workshop, and the positioning performance was validated by tracking the trajectory of the material buffer station moving within the positioning coverage area. The experimental results show that an average positioning accuracy of ∼7 cm was achieved.

RSS ranging based indoor localization in ultra low power wireless network

Advances in ultra low power based wireless networks have enabled developments of a variety of applications including indoor localization systems. Considering the commercial 2.4 GHz wireless devices choose lower and lower power consumption strategy, it is challenging to design an ultra low power based real time indoor localization system. In ultra low power channel conditions, the rapid increase of received signal strength (RSS) measurement variabilities for the minimum RSS value ranges, the spatiotemporally different RSS attenuations, and the common non-line of sight (NLOS) problems make the indoor localization system significantly inefficient. Considering the practical dynamics of ultra low power indoor wireless channel environments, we propose the indoor localization algorithm which can accommodate the environmental dynamics automatically in real time.

Tracking Nosocomial Diseases at Individual Level with a Real-Time Indoor Positioning System.

Our research is motivated by the rapidly-evolving outbreaks of rare and fatal infectious diseases, for example, the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome. In many of these outbreaks, main transmission routes were healthcare facility-associated and through person-to-person contact. While a majority of existing work on modelling of the spread of infectious diseases focuses on transmission processes at a community level, we propose a new methodology to model the outbreaks of healthcare-associated infections (HAIs), which must be considered at an individual level. Our work also contributes to a novel aspect of integrating real-time positioning technologies into the tracking and modelling framework for effective HAI outbreak control and prompt responses. Our proposed solution methodology is developed based on three key components – time-varying contact network construction, individual-level transmission tracking and HAI parameter estimation – and aims to identify the hidden health state of each patient and worker within the healthcare facility. We conduct experiments with a four-month human tracking data set collected in a hospital, which bore a big nosocomial outbreak of the 2003 SARS in Hong Kong. The evaluation results demonstrate that our framework outperforms existing epidemic models for characterizing macro-level phenomena such as the number of infected people and epidemic threshold.

Distributed Machine Learning on Smart-Gateway Network toward Real-Time Smart-Grid Energy Management with Behavior Cognition

Real-time data analytics for smart-grid energy management is challenging with consideration of both occupant behavior profiles and energy profiles. This article proposes a distributed and networked machine-learning platform on smart-gateway-based smart-grid in residential buildings. It can analyze occupant behaviors, provide short-term load forecasting, and allocate renewable energy resources. First, occupant behavior profile is captured by real-time indoor positioning system with WiFi data analytics; and the energy profile is extracted by real-time meter system with electricity load data analytics. Then, the 24-hour occupant behavior profile and energy profile are fused with prediction using an online distributed machine-learning algorithm with real-time data update. Based on the forecasted occupant behavior profile and energy profile, solar energy source is allocated to reduce peak demand on the main electricity power-grid. The whole management flow can be operated on the distributed smart-gateway network with limited computational resources but with a supported general machine-learning engine. Experimental results on occupant behavior extraction show that the proposed algorithm can achieve 91.2% positioning accuracy within 3.64m. Moreover, 50× and 38× speed-up is obtained during data testing and training, respectively, when compared to traditional support vector machine (SVM) method. For short-term load forecasting, it is 14.83% more accurate when compared to SVM-based data analytics. Based on the predicted occupant behavior profile and energy profile, our proposed energy management system can achieve 19.66% more peak load reduction and 26.41% more cost saving as compared to the SVM-based method.

Development of a Novel Indoor Positioning System With mm-Range Precision Based on RF Sensors Network

Indoor localization has become increasingly important for sports analysis, automation, and for mass market products like entertainment systems. For such applications, an increasing accuracy of a few centimeters or even millimeters is desired, but it is a huge challenge to develop a positioning system of that high accuracy in an indoor environment with severe multipath characteristics. A high precision real-time indoor positioning system was designed, implemented, and tested, which is capable of achieving a position accuracy in subcentimeter range. The proposed system implements the positioning based on time difference of arrival technology with the usage of on-off keying modulated ultrahigh frequency signals in order to deal with multipath interference and achieve high positioning precision. This paper describes the design and evaluation of the demonstrator system. The first measurements are performed within a fully furnished laboratory environment and already prove a positioning precision of a radio frequency source with a root-mean-square error of 8 mm. Moreover, the low latency of less than 2 ms and the high update rate of 100 Hz make the system suitable for real-time applications.

Channel Assignment Technique for RF Frequency Reuse in CA-VLC-Based Accurate Optical Indoor Localization

We propose a novel method of frequency assignment for the purpose of efficient RF frequency reuse in an indoor positioning system based on carrier allocation visible light communication. We investigate the performance characteristics of hexagonal, square, and rectangular cell layouts. We experimentally demonstrate a real-time indoor positioning system and make analytical comparisons between different cell layouts to determine the optimum frequency assignment for LED-based coverage.

Positioning Accuracy on Robot Self-localization by Real-time Indoor Positioning System with SS Ultrasonic Waves

Indoor real-time positioning for multiple targets is required to realize human-robot symbiosis. This study firstly presents positioning accuracy on an autonomous mobile robot controlled by 3-D coordinates that is obtained by a real-time indoor positioning system with spread spectrum (SS) ultrasonic signals communicated by code-division multiple access. Although many positioning systems have been investigated, the positioning system with the SS ultrasonic signals can measure identified multiple 3-D positions in every 70 ms with noise tolerance and error within 100 mm. This system is also robust to occlusion and environmental changes. However, thus far, the positioning errors in an autonomous mobile robot, controlled by these systems using the SS ultrasonic signals, have not been evaluated as an experimental study. Therefore, a positioning experiment for trajectory control is conducted using an autonomous mobile robot and our positioning system. The effectiveness of this positioning method for robot self-localization is shown, from this experiment, because the average control error between the target position and the robot’s position at 29 mm is obtained.

Hardware design of a localization system for staff in high-risk manufacturing areas

In this paper, we propose a hardware design for an effective real time indoor localization system for staff working in high-risk manufacturing areas. Because of the special requirements of our system, the chirp spread spectrum (CSS) is the most suitable indoor localization technology. The details of the new localization system are described. The system involves several anchors, tags, and a gateway, all of which use the nanoLOC TRX transceiver (NA5TR1) RF chip (Nanotron Co., Germany), which is based on the CSS technology. To validate the effectiveness of our system, both ranging and localization tests were carried out. The difference between the ranging accuracy indoors and outdoors was small. The localization system can position indoor mobile staff precisely, enabling the establishment of an emergency rescue mechanism.