Wi-Fi Positioning System Research Articles

Autonomous wireless positioning system using crowdsourced Wi-Fi fingerprinting and self-detected FTM stations

Wi-Fi positioning system (WPS) has been proven as an effective way to realize universal indoor navigation for smart city-related applications. The localization ability of the existing WPS is affected by the low precision of the crowdsourced database and the unknown location of local wireless stations. This paper develops an integrated indoor localization framework using crowdsourced Wi-Fi fingerprinting and self-detected Wi-Fi Fine Time Measurement (FTM) stations (IL-CFSW). A novel crowdsourced mobile sensors data modeling algorithm with self-calibrated parameters is proposed, and modeled trajectories are further segmented and matched with the existing pedestrian indoor network to enhance the performance of the final database generation. Furthermore, the iteration unscented Kalman filter (iUKF) is adopted to recognize the position and calibrate the bias of existing Wi-Fi FTM anchors combined with the hybrid distance measurement model. Finally, an enhanced particle filter with error ellipse constraint is developed to fuse different location sources and indoor network information. Comprehensive experimental results present that the developed IL-CFSW can achieve autonomous 3D indoor localization performance in multi-floor contained scenes and meter-level positioning accuracy is achieved with the consideration of pedestrian indoor network information.

Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V) and its first demonstration using directional information from tracking detectors

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client’s muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that “sub-cm level” navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations.

Passenger spatiotemporal distribution prediction in airport terminals based on insect intelligent building architecture and its contribution to fresh air energy saving

As the essential infrastructure and important transportation hubs, airport terminals play a strategic role in sustainable cities and urbanization. Due to the strict service requirement and round-the-clock operation, the high energy consumption poses unprecedented challenges to developing environment-friendly airport terminals. Actually, passenger flow as a root cause decides the startup and operation of service facilities including air-conditioning systems. But due to spatial connectivity, parameter coupling, and passenger mobility, resource allocation and system tuning have not been effectively related to passenger travel. In this work, Guangzhou Baiyun International Airport Terminal 2 was taken as an object, and a passenger spatiotemporal distribution model was developed and its contribution to fresh air energy saving was estimated. Among them, zone partition and expression rules were formulated based on insect intelligent building (I2B) architecture to construct Wi-Fi positioning system; Next, for domestic and international passenger, their arrival patterns were fitted by Chi-Square distribution with R2 > 0.993, and their spatiotemporal distribution was predicted with R2 over 0.74 and 0.67 respectively; Lastly, a hybrid reset strategy for fresh air volume set-point combing fuzzy and linear rules was proposed with results showing that HVAC systems running on variable occupancy would lead to considerable energy savings.

Indoor Localization Based on Factor Graphs: A Unified Framework

Indoor localization is of pivotal significance for a wide variety of services in the context of the Internet of Things (IoT). Both ranging-based and fingerprint-based localization techniques are promising for employment in harsh indoor environments. Hence, we propose a unified framework based on factor graphs for ubiquitous high-accuracy indoor localization. Our unified framework efficiently integrates ranging and fingerprinting for striking an appealing accuracy versus deployment cost tradeoff, where the crowdsourcing required for the construction of fingerprinting databases can also be addressed with little human intervention. By intrinsically amalgamating the global grid sampling and the regularized importance-resampling techniques, a nonparametric belief propagation algorithm is proposed for achieving the accurate position estimation at the cost of a moderate computational complexity. For improving the robustness to environmental variations, a likelihood-ratio-based approach is employed to detect ranging outliers. Moreover, a low-complexity serial scheduling scheme defined over factor graphs is designed for real-time localization. We design a hybrid ultrawide bandwidth and Wi-Fi localization system relying on off-the-shelf commercial devices and evaluate the proposed unified framework in a typical office building. Our experimental results show that the proposed algorithm outperforms the existing state-of-the-art methods and it is capable of achieving submeter localization accuracy.

DTTV Localization with Fingerprinting Technique and Clean Algorithm Based on Measurement Data

Digital terrestrial television (DTTV) technology has been developed and used to broadcast the television program. A numerous applications have been developed for the additional feature used with DTTV. One of these features that can be used which is the localization system with DTTV broadcasting. The advantage of DVB-T2 broadcasting channel for localization technology which very wide coverage area that covered whether outdoor and indoor environment. In the present there are divers methodologies to locate the position of an object or user such as the Global Positioning System (GPS), Cellular Positioning System (CPS) and Wi-Fi Positioning System (WPS). Nowadays there are various application that used for monitoring and controlling such as a water level sensor system, a traffic control system, an intrusion monitoring system etc. that consists of the localization system. The received data can’t be useful without the accuracy location. The mentioned foregoing system still have a limitation in some environment such as the GPS signal is not accessible to some environments, the CPS signal is based on a cell phone tower and the WPS is based on Wi-Fi hotspot. Therefore, the accuracy of localization is decreased. In order to overcome the foregoing limitation of these three systems the complementary remedy the poor coverage is required. The objective of this research is to improve the DVB-T2 propagation channel by a Clean algorithm to eliminate the noise propagation channel for an accuracy of localization system. This technique is very useful for localization analysis in DTTV technology. The distinctive advantage of the DTTV localization is the wide coverage of signal whether an indoor or outdoor environment. Moreover, when the Clean algorithm has been used the noise in propagation channel has been eliminated lead to the accuracy of location receive.

Reliable Wi-Fi Indoor Localization in Case of AP Loss by Using Integrated Model Based on Signal Anomaly Detector and Signal Distance Corrector

When developing a Wi-Fi indoor positioning system in a real-world environment, the problems we have to face are that some access points’ signal strength fluctuates extensively or even loses contact due to the cybersecurity threats, leading to the fact that the indoor location system cannot get reliable application in a real-world environment. To solve this problem, we propose a new integrated model based on signal anomaly detector and signal distance corrector to provide reliable position estimation when the access points’ signal is lost under cybersecurity threats. The signal anomaly detector improves recognition capability of the uncertain signal and noise, while the signal distance corrector improves the robustness and fault tolerance of the highly variable Wi-Fi signals. To fully reflect the performance of the proposed method, experiments have been carried out in the real environment of indoor parking lots. The results show that the proposed integrated model successfully provides reliable position estimation when the access points are lost under cybersecurity threats.

An Android and Arduino Based Low-Cost Educational Robot with Applied Intelligent Control and Machine Learning

Applied Science requires testbeds to carry out experiments and validate in practice the results of the application of the methods. This article presents a low-cost (35–40 euros) educational mobile robot, based on Android and Arduino, integrated with Robot Operating System (ROS), together with its application for learning and teaching in the domain of intelligent automatic control, computer vision and Machine Learning. Specifically, the practical application to visual path tracking integrated with a Fuzzy Collision Risk system, that avoids collision with obstacles ahead, is shown. Likewise, a Wi-Fi positioning system is presented, which allows identifying in which room the robot is located, based on self-collected data and Machine Learning.

Preserving Privacy in WiFi Localization With Plausible Dummy Locations

Benefiting from the development of wireless communication, WiFi localization plays a vital role in various mobile applications. However, users’ locations are breached by untrusted localization servers, thus disclosing users’ location privacy and other sensitive personal information. Existing works on WiFi localization preservation employed homomorphic encryption, which incurs extremely high overheads. On the other hand, lightweight methods like $k$ -anonymity and dummy-based approaches are hardly used for privacy preservation in WiFi localization scenarios due to their inherent drawbacks, i.e., $k$ -anonymity approaches rely on trusted third parties while dummy-based approaches are susceptible to spatio-temporal correlation attacks. To design an effective yet lightweight WiFi localization privacy algorithm, this paper proposes to reinforce dummy techniques with plausible dummy location to resist the attacks. This study presents a novel algorithm, referred to as Location Preservation Algorithm with Plausible Dummies (LPPD), which is the first attempt towards WiFi localization privacy preservation using dummy techniques. The benefits of the algorithm are three-fold: First, it is lightweight. Second, it does not rely on trusted third parties. Third, it can resist spatio-temporal correlation attacks. Moreover, this study implements a WiFi localization system that integrates the proposed privacy-preserving algorithm with a WiFi fingerprint based indoor localization algorithm. Experiments results are presented to demonstrate the effectiveness and efficiency of our approach when compared with existing algorithms.

Discovering locations and habits from human mobility data

Human mobility patterns are associated with many aspects of our life. With the increase of the popularity and pervasiveness of smartphones and portable devices, the Internet of Things (IoT) is turning into a permanent part of our daily routines. Positioning technologies that serve these devices such as the cellular antenna (GSM networks), global navigation satellite systems (GPS), and more recently the WiFi positioning system (WPS) provide large amounts of spatio-temporal data in a continuous way (data streams). In order to understand human behavior, the detection of important places and the movements between these places is a fundamental task. That said, the proposal of this work is a method for discovering user habits over mobility data without any a priori or external knowledge. Our approach extends a density-based clustering method for spatio-temporal data to identify meaningful places the individuals’ visit. On top of that, a Gaussian mixture model (GMM) is employed over movements between the visits to automatically separate the trajectories accordingly to their key identifiers that may help describe a habit. By regrouping trajectories that look alike by day of the week, length, and starting hour, we discover the individual’s habits. The evaluation of the proposed method is made over three real-world datasets. One dataset contains high-density GPS data and the others use GSM mobile phone data with 15-min sampling rate and Google Location History data with a variable sampling rate. The results show that the proposed pipeline is suitable for this task as other habits rather than just going from home to work and vice versa were found. This method can be used for understanding person behavior and creating their profiles revealing a panorama of human mobility patterns from raw mobility data.

A Wi-Fi Positioning System for Material Transport in Greenhouses

A Wi-Fi Positioning System for Material Transport in Greenhouses

Integration of positioning and activity context information for lifelog in urban city area

<h3>Abstract</h3> Smartphone-based Lifelog (automatically annotating the users’ daily experience from multisensory streams on smartphones) is in great need. Accurate positioning under any situation is one of the most significant techniques for a desirable Lifelog. This paper proposes to detect location-related activities and use the activity information to improve positioning accuracy. In the proposed system, a human activity recognition module is developed to extract location-related activities from multisensory streams of smartphones. After that, the proposed system integrates activity information with PDR-based positioning results in a context-based map-matching framework. The developed system can be used for both outdoor and indoor scenarios. Moreover, the developed indoor positioning method is used to determine the positions of calibration points automatically in an auto-calibration Wi-Fi positioning system. The proposed methods achieve 3.1-m accuracy in outdoor and average 2.2-m accuracy in indoor situations.

A Drift-of-Stay Pattern Extraction Method for Indoor Pedestrian Trajectories for the Error and Accuracy Assessment of Indoor Wi-Fi Positioning

The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.

Wearable Indoor Tracking Device using Wi-Fi Positioning System with PPG Sensor and Android Application

Many inventions that manifest the required qualities for the improvement of the search and rescue operations are made. However, the present inventions do not cover accurate tracking of the victims which contributes to a delay during operation. Thus, a Wearable Indoor Tracking Device using Wi-Fi Positioning System with PPG Sensor and Android Application is developed to uplift the limitations of the devices present today which can resolve the delay of search and rescue operation. This project focuses on detecting the specific location of the victims which includes distance and direction from rescuer’s perspective as well as the victim's status. Moreover, the location and status of the victim can be viewed simultaneously through an Android Application. To achieve the tracking technology, trilateration technique is used. It manipulates the calculated distance of the beacons by finding their intersection. To get the measured distance the researchers use an algorithm named Free Space Path Loss wherein it converts the Wi-Fi strength into distance. For the direction, slope formula is used to determine the measured angle between the rescuer and the victim. Therefore, by making this wearable device, the search and rescue operation will increase its accuracy to 80% in finding the distance of the rescuer and the victim and by 100% in finding the direction of the rescuer from the victim.

Wi-Fi-Based Effortless Indoor Positioning System Using IoT Sensors.

Wi-Fi positioning based on fingerprinting has been considered as the most widely used technology in the field of indoor positioning. The fingerprinting database has been used as an essential part of the Wi-Fi positioning system. However, the offline phase of the calibration involves a laborious task of site analysis which involves costs and a waste of time. We offer an indoor positioning system based on the automatic generation of radio maps of the indoor environment. The proposed system does not require any effort and uses Wi-Fi compatible Internet-of-Things (IoT) sensors. Propagation loss parameters are automatically estimated from the online feedback of deployed sensors and the radio maps are updated periodically without any physical intervention. The proposed system leverages the raster maps of an environment with the wall information only, against computationally extensive techniques based on vector maps that require precise information on the length and angles of each wall. Experimental results show that the proposed system has achieved an average accuracy of 2 m, which is comparable to the survey-based Wi-Fi fingerprinting technique.

Unambiguous Association of Crowd-Sourced Radio Maps to Floor Plans for Indoor Localization

In many survey-free Wi-Fi indoor localization systems, including the Adaptive indoor Wi-Fi Positioning System (AWPS) we proposed earlier, there is a need to associate unlabeled measurements to a floor plan. In this paper, we address the problem of how to associate a topological radio graph generated by crowd-sourced RSS measurements to an isomorphic Euclidean graph representing the physical space which may come from a geographic information system (GIS) or through automatic image analysis of a paper floor plan. We introduce the concept of Minimum Symmetric Structures (MSS) and Co-rooted Congruent Structures (COCS) as new ways of characterizing automorphism which prevent unambiguous association, and present a structure analysis algorithm for detecting these structures. Then, we derive rules on the number and locations of markers, or RSS measurements with location labels, needed for resolving the automorphism. Applying the analysis proposed in this paper to hypothetical floor plans as well as floor plans used in AWPS and other existing systems, we will demonstrate that often very few location labels are needed in a SLAM system.

A new perspective of environmental behaviour study: a brief introduction of Wi-Fi indoor positioning system

Environment behaviour study is fundamental to architectural and urban design. The traditional methods have difficulty in obtaining quantitative data on a large-scale, which indirectly hinders the intelligent process of building environment. The newly developed Wi-Fi indoor positioning system can provide a large number of effective data to environment behaviour study. This study gives a brief introduction to the Wi-Fi positioning system and analyses the abundant information contained in its data. On this basis, this study takes an innovation incubator as an example, trying to explore human interaction in the environment with the tool of social network analysis. Finally, this study provides a simple framework for implementing smart home with positioning data. The conclusions highlight the advantages and limitation of Wi-Fi positioning system, as well as the promising prospect in the smart home.

Modified WiFi-RSS Fingerprint Technique to locate Indoors-Smartphones: FENG building at Koya University as a case study

Positioning system used for different purposes and different services, many researches are going on to find a more accurate position with low error within high performance. There are many localization solutions with different architectures, configurations, accuracies and reliabilities for both outdoors and indoors. For example, Global Navigation Satellite System (GNSS) technology has been used for outdoors. Global Positioning System (GPS) is one of the most common outdoors tracking solutions in the world, for outdoors, however, when indoors; it could not be accurately tracked users by using a GPS system. This is because, when users enters into indoors the GPS signals will no longer available due to blocked by the roof of buildings and it is no longer considered as a viable option. WiFi Positioning System (WPS) can be used as an alternative solution to define users’ position, especially when GPS signal is not available. Further, WPS is a low cost solution, because there is no need to deploying WiFi Access Points (WAPs) in the vicinity, as they are installed to access the Internet. In this paper, specifically, WiFi-RSS Fingerprinting technique is used to locate smartphones using WAPs signals with a modified calculation. The new modified calculation is to dynamic weighting of the WAPs RSS values based on the real-live indoors structure. The achieved positioning accuracy, based on several trial experiments, is up to 6 meters via the implemented algorithm in the MALTAB.

1-2 Wi-Fi位置推定を利用した複合センサトラッキング(第1部門 メディア工学1)

1-2 Wi-Fi位置推定を利用した複合センサトラッキング(第1部門 メディア工学1)

Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario.

Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

A novel approach towards utilizing Dempster Shafer fusion theory to enhance WiFi positioning system accuracy

WiFi positioning system (WPS) is the most opted technology to provide positioning information indoors. Among the two WPS approaches: Radio-based propagation model and Fingerprinting method, latter is the highly preferred approach for WPS wherein obtained RSSI signatures are matched statistically with the pre-calibrated radio map to localize the user. Existing approaches assume the RSSI distribution to be Gaussian and then utilize Bayesian approaches to derive the position information.We present a novel approach wherein, firstly, RSSI distribution using kernel density estimation method is approximated via Support Vector Regression (SVR). Secondly, a method is presented to obtain the fused probability mass corresponding to different access points using Dempster Shafer (DS) theory. The fused probability mass thus obtained is used to identify the highly probable points or location of a user over pre-calibrated points. Experimental results demonstrated that the proposed two-step sequential approach is able to limit the 90th percentile localization error to be within meter level. Further the localization accuracy improved to about 88% using our SVR approach in comparison to Gaussian assumption.