37 research outputs found
CrowdFusion: Multi-Signal Fusion SLAM Positioning Leveraging Visible Light
With the fast development of location-based services, an ubiquitous indoor positioning approach with high accuracy and low calibration has become increasingly important. In this work, we target on a crowdsourcing approach with zero calibration effort based on visible light, magnetic field and WiFi to achieve sub-meter accuracy. We propose a CrowdFusion Simultaneous Localization and Mapping (SLAM) comprised of coarse-grained and fine-grained trace merging respectively based on the Iterative Closest Point (ICP) SLAM and GraphSLAM. ICP SLAM is proposed to correct the relative locations and directions of crowdsourcing traces and GraphSLAM is further adopted for fine-grained pose optimization. In CrowdFusion SLAM, visible light is used to accurately detect loop closures and magnetic field to extend the coverage. According to the merged traces, we construct a radio map with visible light and WiFi fingerprints. An enhanced particle filter fusing inertial sensors, visible light, WiFi and floor plan is designed, in which visible light fingerprinting is used to improve the accuracy and increase the resampling/rebooting efficiency. We evaluate CrowdFusion based on comprehensive experiments. The evaluation results show a mean accuracy of 0.67m for the merged traces and 0.77m for positioning, merely replying on crowdsourcing traces without professional calibration
Indoor positioning model based on people effect and ray tracing propagation
WLAN-fingerprinting has been highlighted as the preferred technology in an Indoor Positioning System (IPS) due to its accurate positioning results and minimal infrastructure cost. However, the accuracy of IPS fingerprinting is highly influenced by the fluctuation in signal strength as a result of encountering obstacles. Many researchers have modelled static obstacles such as walls and ceilings, but hardly any have modelled the effect of people presence as an obstacle although the human body significantly impacts signal strength. Hence, the people presence effect must be considered to obtain highly accurate positioning results. Previous research proposed a model that only considered the direct path between the transmitter and the receiver. However, for indoor propagation, multipath effects such as reflection can also have a significant influence, but were not considered in past work. Therefore, this research proposes an accurate indoor positioning model that considers people presence using a ray tracing (AIRY) model in a dynamic environment which relies on existing infrastructure. Three solutions were proposed to construct AIRY: an automatic radio map using ray tracing (ARM-RT), a new human model in ray tracing (HUMORY), and a people effect constant for received signal strength indicator (RSSI) adaptation. At the offline stage, 30 RSSIs were recorded at each point using a smartphone to create a radio map database (523 points). The real-time RSSI was then compared to the radio map database at the online stage using MATLAB software to determine the user position (65 test points). The proposed model was tested at Level 3 of Razak Tower, UTM Kuala Lumpur (80 × 16 m). To test the influence of people presence, the number, position, and distance of the people around the mobile device (MD) were varied. The results showed that the closer the people were to the MD in both the Line of Sight (LOS) and Non-LOS position, the greater the decrease in RSSI, in which the increment number of people will increase the amount of reflection signals to be blocked. The signal strength reduction started from 0.5 dBm with two people and reached 0.9 dBm with seven people. In addition, the ray tracing model produced smaller errors on RSSI prediction than the multi-wall model when considering the effect of people presence. The k-nearest neighbour (KNN) algorithm was used to define the position. The initial accuracy was improved from 2.04 m to 0.57 m after people presence and multipath effects were considered. In conclusion, the proposed model successfully increased indoor positioning accuracy in a dynamic environment by overcoming the people presence effect
WLAN-paikannuksen elinkaaren tukeminen
The advent of GPS positioning at the turn of the millennium provided consumers with worldwide access to outdoor location information. For the purposes of indoor positioning, however, the GPS signal rarely penetrates buildings well enough to maintain the same level of positioning granularity as outdoors.
Arriving around the same time, wireless local area networks (WLAN) have gained widespread support both in terms of infrastructure deployments and client proliferation. A promising approach to bridge the location context then has been positioning based on WLAN signals. In addition to being readily available in most environments needing support for location information, the adoption of a WLAN positioning system is financially low-cost compared to dedicated infrastructure approaches, partly due to operating on an unlicensed frequency band. Furthermore, the accuracy provided by this approach is enough for a wide range of location-based services, such as navigation and location-aware advertisements.
In spite of this attractive proposition and extensive research in both academia and industry, WLAN positioning has yet to become the de facto choice for indoor positioning. This is despite over 20 000 publications and the foundation of several companies. The main reasons for this include: (i) the cost of deployment, and re-deployment, which is often significant, if not prohibitive, in terms of work hours; (ii) the complex propagation of the wireless signal, which -- through interaction with the environment -- renders it inherently stochastic; (iii) the use of an unlicensed frequency band, which means the wireless medium faces fierce competition by other technologies, and even unintentional radiators, that can impair traffic in unforeseen ways and impact positioning accuracy.
This thesis addresses these issues by developing novel solutions for reducing the effort of deployment, including optimizing the indoor location topology for the use of WLAN positioning, as well as automatically detecting sources of cross-technology interference. These contributions pave the way for WLAN positioning to become as ubiquitous as the underlying technology.GPS-paikannus avattiin julkiseen käyttöön vuosituhannen vaihteessa, jonka jälkeen sitä on voinut käyttää sijainnin paikantamiseen ulkotiloissa kaikkialla maailmassa. Sisätiloissa GPS-signaali kuitenkin harvoin läpäisee rakennuksia kyllin hyvin voidakseen tarjota vastaavaa paikannustarkkuutta.
Langattomat lähiverkot (WLAN), mukaan lukien tukiasemat ja käyttölaitteet, yleistyivät nopeasti samoihin aikoihin. Näiden verkkojen signaalien käyttö on siksi alusta asti tarjonnut lupaavia mahdollisuuksia sisätilapaikannukseen. Useimmissa ympäristöissä on jo valmiit WLAN-verkot, joten paikannuksen käyttöönotto on edullista verrattuna järjestelmiin, jotka vaativat erillisen laitteiston. Tämä johtuu osittain lisenssivapaasta taajuusalueesta, joka mahdollistaa kohtuuhintaiset päätelaitteet. WLAN-paikannuksen tarjoama tarkkuus on lisäksi riittävä monille sijaintipohjaisille palveluille, kuten suunnistamiselle ja paikkatietoisille mainoksille.
Näistä lupaavista alkuasetelmista ja laajasta tutkimuksesta huolimatta WLAN-paikannus ei ole kuitenkaan pystynyt lunastamaan paikkaansa pääasiallisena sisätilapaikannusmenetelmänä. Vaivannäöstä ei ole puutetta; vuosien saatossa on julkaistu yli 20 000 tieteellistä artikkelia sekä perustettu useita yrityksiä. Syitä tähän kehitykseen on useita. Ensinnäkin, paikannuksen pystyttäminen ja ylläpito vaativat aikaa ja vaivaa. Toiseksi, langattoman signaalin eteneminen ja vuorovaikutus ympäristön kanssa on hyvin monimutkaista, mikä tekee mallintamisesta vaikeaa. Kolmanneksi, eri teknologiat ja laitteet kilpailevat lisenssivapaan taajuusalueen käytöstä, mikä johtaa satunnaisiin paikannustarkkuuteen vaikuttaviin tietoliikennehäiriöihin.
Väitöskirja esittelee uusia menetelmiä joilla voidaan merkittävästi pienentää paikannusjärjestelmän asennuskustannuksia, jakaa ympäristö automaattisesti osiin WLAN-paikannusta varten, sekä tunnistaa mahdolliset langattomat häiriölähteet. Nämä kehitysaskeleet edesauttavat WLAN-paikannuksen yleistymistä jokapäiväiseen käyttöön
Advanced Location-Based Technologies and Services
Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements
Self-healing radio maps of wireless networks for indoor positioning
Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real
nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente,
a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais
possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos,
sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os
sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados
para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições
conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação,
adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente
em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam
em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que
seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento
para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas
de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de
movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas
duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a
localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar
quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração
nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências
sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços
de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which
benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring,
improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be
exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and
interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they
represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi-
Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation
effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated
to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming
and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly
present in industrial environments, can be explored to help build and maintain radio maps, as long as it
is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial
vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked
using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the
self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are
proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when
Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human
effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this
solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and
analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018
Evaluating Sensor Data in the Context of Mobile Crowdsensing
With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach
User Experience Enhancement on Smartphones using Wireless Communication Technologies
학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 박세웅.Recently, various sensors as well as wireless communication technologies such as
Wi-Fi and Bluetooth Low Energy (BLE) have been equipped with smartphones. In
addition, in many cases, users use a smartphone while on the move, so if a wireless
communication technologies and various sensors are used for a mobile user, a better
user experience can be provided. For example, when a user moves while using Wi-Fi,
the user experience can be improved by providing a seamless Wi-Fi service. In addition,
it is possible to provide a special service such as indoor positioning or navigation
by estimating the users mobility in an indoor environment, and additional services
such as location-based advertising and payment systems can also be provided. Therefore,
improving the user experience by using wireless communication technology and
smartphones sensors is considered to be an important research field in the future.
In this dissertation, we propose three systems that can improve the user experience
or convenience by usingWi-Fi, BLE, and smartphones sensors: (i) BLEND: BLE
beacon-aided fast Wi-Fi handoff for smartphones, (ii) PYLON: Smartphone based Indoor
Path Estimation and Localization without Human Intervention, (iii) FINISH:
Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance.
First, we propose fast handoff scheme called BLEND exploiting BLE as secondary
radio. We conduct detailed analysis of the sticky client problem on commercial smartphones
with experiment and close examination of Android source code. We propose
BLEND, which exploits BLE modules to provide smartphones with prior knowledge
of the presence and information of APs operating at 2.4 and 5 GHz Wi-Fi channels.
BLEND operating with only application requires no hardware and Android source code
modification of smartphones.We prototype BLEND with commercial smartphones and
evaluate the performance in real environment. Our measurement results demonstrate
that BLEND significantly improves throughput and video bitrate by up to 61% and
111%, compared to a commercial Android application, respectively, with negligible
energy overhead.
Second, we design a path estimation and localization system, termed PYLON,
which is plug-and-play on Android smartphones. PYLON includes a novel landmark
correction scheme that leverages real doors of indoor environments consisting of floor
plan mapping, door passing time detection and correction. It operates without any user
intervention. PYLON relaxes some requirements for localization systems. It does not
require any modifications to hardware or software of smartphones, and the initial location
of WiFi APs, BLE beacons, and users. We implement PYLON on five Android
smartphones and evaluate it on two office buildings with the help of three participants
to prove applicability and scalability. PYLON achieves very high floor plan mapping
accuracy with a low localization error.
Finally, We design a fully-automated navigation system, termed FINISH, which
addresses the problems of existing previous indoor navigation systems. FINISH generates
the radio map of an indoor building based on the localization system to determine
the initial location of the user. FINISH relaxes some requirements for current
indoor navigation systems. It does not require any human assistance to provide navigation
instructions. In addition, it is plug-and-play on Android smartphones. We implement
FINISH on five Android smartphones and evaluate it on five floors of an office
building with the help of multiple users to prove applicability and scalability. FINISH
determines the location of the user with extremely high accuracy with in one step.
In summary, we propose systems that enhance the users convenience and experience
by utilizing wireless infrastructures such as Wi-Fi and BLE and various smartphones
sensors such as accelerometer, gyroscope, and barometer equipped in smartphones.
Systems are implemented on commercial smartphones to verify the performance
through experiments. As a result, systems show the excellent performance that
can enhance the users experience.1 Introduction 1
1.1 Motivation 1
1.2 Overview of Existing Approaches 3
1.2.1 Wi-Fi handoff for smartphones 3
1.2.2 Indoor path estimation and localization 4
1.2.3 Indoor navigation 5
1.3 Main Contributions 7
1.3.1 BLEND: BLE Beacon-aided Fast Handoff for Smartphones 7
1.3.2 PYLON: Smartphone Based Indoor Path Estimation and Localization with Human Intervention 8
1.3.3 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 9
1.4 Organization of Dissertation 10
2 BLEND: BLE Beacon-Aided FastWi-Fi Handoff for Smartphones 11
2.1 Introduction 11
2.2 Related Work 14
2.2.1 Wi-Fi-based Handoff 14
2.2.2 WPAN-aided AP Discovery 15
2.3 Background 16
2.3.1 Handoff Procedure in IEEE 802.11 16
2.3.2 BSS Load Element in IEEE 802.11 16
2.3.3 Bluetooth Low Energy 17
2.4 Sticky Client Problem 17
2.4.1 Sticky Client Problem of Commercial Smartphone 17
2.4.2 Cause of Sticky Client Problem 20
2.5 BLEND: Proposed Scheme 21
2.5.1 Advantages and Necessities of BLE as Secondary Low-Power Radio 21
2.5.2 Overall Architecture 22
2.5.3 AP Operation 23
2.5.4 Smartphone Operation 24
2.5.5 Verification of aTH estimation 28
2.6 Performance Evaluation 30
2.6.1 Implementation and Measurement Setup 30
2.6.2 Saturated Traffic Scenario 31
2.6.3 Video Streaming Scenario 35
2.7 Summary 38
3 PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention 41
3.1 Introduction 41
3.2 Background and Related Work 44
3.2.1 Infrastructure-Based Localization 44
3.2.2 Fingerprint-Based Localization 45
3.2.3 Model-Based Localization 45
3.2.4 Dead Reckoning 46
3.2.5 Landmark-Based Localization 47
3.2.6 Simultaneous Localization and Mapping (SLAM) 47
3.3 System Overview 48
3.3.1 Notable RSSI Signature 49
3.3.2 Smartphone Operation 50
3.3.3 Server Operation 51
3.4 Path Estimation 52
3.4.1 Step Detection 52
3.4.2 Step Length Estimation 54
3.4.3 Walking Direction 54
3.4.4 Location Update 55
3.5 Landmark Correction Part 1: Virtual Room Generation 56
3.5.1 RSSI Stacking Difference 56
3.5.2 Virtual Room Generation 57
3.5.3 Virtual Graph Generation 59
3.5.4 Physical Graph Generation 60
3.6 Landmark Correction Part 2: From Floor Plan Mapping to Path Correction 60
3.6.1 Candidate Graph Generation 60
3.6.2 Backbone Node Mapping 62
3.6.3 Dead-end Node Mapping 65
3.6.4 Final Candidate Graph Selection 66
3.6.5 Door Passing Time Detection 68
3.6.6 Path Correction 70
3.7 Particle Filter 71
3.8 Performance Evaluation 73
3.8.1 Implementation and Measurement Setup 73
3.8.2 Step Detection Accuracy 77
3.8.3 Floor Plan Mapping Accuracy 77
3.8.4 Door Passing Time 78
3.8.5 Walking Direction and Localization Performance 81
3.8.6 Impact of WiFi AP and BLE Beacon Number 84
3.8.7 Impact of Walking Distance and Speed 84
3.8.8 Performance on Different Areas 87
3.9 Summary 87
4 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 91
4.1 Introduction 91
4.2 Related Work 92
4.2.1 Localization-based Navigation System 92
4.2.2 Peer-to-peer Navigation System 93
4.3 System Overview 93
4.3.1 System Architecture 93
4.3.2 An Example for Navigation 95
4.4 Level Change Detection and Floor Decision 96
4.4.1 Level Change Detection 96
4.5 Real-time navigation 97
4.5.1 Initial Floor and Location Decision 97
4.5.2 Orientation Adjustment 98
4.5.3 Shortest Path Estimation 99
4.6 Performance Evaluation 99
4.6.1 Initial Location Accuracy 99
4.6.2 Real-Time Navigation Accuracy 100
4.7 Summary 101
5 Conclusion 102
5.1 Research Contributions 102
5.2 Future Work 103
Abstract (In Korean) 118
감사의 글Docto
Recent Advances in Indoor Localization Systems and Technologies
Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods