ViFi: virtual fingerprinting WiFi-based indoor positioning via multi-wall multi-floor propagation model

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a sevenfold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed

Metoda za določanje položaja v prostoru na osnovi signalov WiFi in modela zgradbe

WiFi indoor localization is a difficult task due to the variability of the WiFi signal. Consequently, there have been many attempts to develop WiFi-based methods which were aided by some other means to provide accurate indoor localization. Technologies like dead reckoning and IMU sensors, crowd utilization and pattern matching, specialized Li-Fi hardware and directional antennas, etc. were used to aid the WiFi in order to develop more accurate and stable methods. The main disadvantage of such methods lies in difficult deployments due to technologies and requirements: Dead-reckoning-aided methods are not suitable for stationary objects, methods leveraging groups of people and many individuals are not best suited for home environment, Li-Fi assisted methods require mobile terminals to provide Li-Fi connectivity and therefore rule out mobile phones as the most common terminal. In the past, many fingerprinting methods were proposedthese require a survey in the area of localization during the setup phase. Unfortunately, the majority of fingerprinting-based methods do not address issues of long-term stability of the WiFi signals. Thus, they face accuracy issues a few days after the calibrationfrequent, costly and time-consuming recalibration procedures are used to address these issues. Model-based methods try to eliminate calibration procedures by simulating signal propagation. Many of the methods assume at least some parameters of propagation as fixed and therefore poorly address the issues of WiFi’s variability and long-term stability. A pure WiFi model-based method that successfully addresses these issues and requires a mobile terminal only for emitting or receiving the WiFi signals is the ultimate goal of the WiFi indoor localization. This thesis presents a novel indoor localization method, with the main intent of addressing the issues of real-world applicability. Therefore, we focused on developing a method with accuracy comparable to the state-of-the-art methods, while reducing the complexity of deployment and minimizing the required maintenance for long-term deployments. The presented method is a model-based method, implementing self-adaptive operability, i.e. it does not require any human intervention. The thesis discusses in detail the topics of the long-term stability of the WiFi signal, receiving vs. transmitting methods, the future WiFi standards, comparability of the methods and architectural aspects with respect to real-world applicability of the localization methods. Our presented method estimates the parameters of signal propagation, by knowing the positions of the access points, the architectural floor plan with the dividing walls and by monitoring power of the packets travelling between the access points. From this data propagation parameters defined in propagation model are inferred in an online manner. A device trying to define its position captures power information of the packets sent by the access points. Devices’ information on the observed power is used to determine its position by an algorithm run on the localization server. The presented WiFi method is primarily developed and evaluated in single- and multi-room office environments. The method’s ability to be easily applicable in any environment is emphasized by its evaluation in two different environments – office and residential. Between the two, no parameters were modified, thus evaluations indicate universality of the method. Furthermore, we provide evaluation also in narrow hallway because in the field of indoor localization such evaluation environments are common practice. During the evaluation of our proposed method in the office environment, we obtained an average error of 2.63 m and 3.22 m for the single- and multi-room environments respectively. Second evaluation was performed in the residential environment, for which the method or any of the parameters were not modified. Our method achieved an average evaluation error of 2.65 m with standard deviation of 1.51 m, during the four independent evaluations, each consisting of 17 localization points. High accuracy of localization, with acknowledgement to the intricate and realistic multi-room floor plan with different types of walls, realistic furniture and real-world signal interference from the neighboring apartments, proves the method’s applicability to the real-world environment. Evaluation accuracy can be compared to the state-of-the-art methods, while our easily-applicable method requires far less complicated setup procedures and/or hardware requirements. In the second part of the thesis, we generalize the WiFi method to be applicable to the frequencies other than 2.4 GHz WiFi. By defining a fusion algorithm which considers accuracy of the individual frequencies, we have defined the MFAM method: Multiple Frequency Adaptive Model-Based Indoor Localization Method. The MFAM is one of the first purely model-based approaches capable of utilizing multiple frequencies simultaneously. The MFAM method was evaluated in residential environment on two frequency bands: 868 MHz and 2.4 GHz. The method retained positive properties of our WiFi approach (e.g. pure model-based, self-adaptive operability, wide applicability on affordable hardware), while improving the accuracy due to multi-frequency fusion. The usage of multiple frequencies improved the average error of localization from 2.65 m, while using only the WiFi, down to 2.16 m, in the case of multi-frequency fusion, thus improving localization accuracy for 18%. Similar improvements were observed also for the standard deviation. Although the accuracy of the presented WiFi and MFAM methods is comparable if not better than the state-of-the-art methods, one of the most important achievements of our work is the applicability of the method to the real-world situations and its long-term stability. The definition of our method ensures that the accuracy of the method will be the same at the time it is initialized, as well as days later, without any human interaction.Določanje lokacije znotraj prostorov na podlagi WiFi signalov je zaradi variabilnosti signala WiFi težka naloga. Posledično je bilo v preteklosti veliko poizkusov razvoja WiFi metod, ki uporabljajo dodatne informacije za natančno lokalizacijo. Ocena prehojene poti in inercijski senzorji, uporaba množice ljudi in ujemanje vzorcev, tehnologija Li-Fi in usmerjene antene itd. je le nekaj v preteklosti uporabljenih načinov za dopolnitev WiFi signalov pri razvoju natančnih in stabilnih metod. Glavna slabost takih metod se kaže v zahtevnem uvajanju zaradi uporabljenih tehnologij in zahtev: metode ocene prehojene poti niso primerne za stacionarne predmete, metode, ki uporabljajo množice ljudi, niso primerne za domače okolje, Li-Fi metode zahtevajo, da so mobilni terminali opremljeni z ustreznimi sprejemniki in tako izključijo mobilne telefone kot terminale. V preteklosti so bile predlagane številne metode, ki bazirajo na prstnih odtisih signalov. Te metode zahtevajo kalibracijske meritve v prostoru v fazi implementacije metode. Večina teh metod ne naslovi vprašanj dolgoročne stabilnosti WiFi signalov, posledično se soočajo s težavami zaradi natančnosti nekaj dni po kalibraciji. Pogoste, drage in časovno potratne ponovne kalibracije so potrebne za reševanje teh težav. Metode, temelječe na matematičnih modelih, poskušajo eliminirati kalibracijske postopke s simulacijo širjenja signala. Večina teh metod vseeno privzame vsaj nekatere parametre propagacije kot fiksne in tako slabo naslovi variabilnost WiFi signalov in dolgoročno stabilnost. Izključno WiFi modelna metoda, ki uspešno naslovi te težave in zahteva, da mobilni terminal samo oddaja ali sprejema WiFi signale, je končni cilj WiFi metod za določanje položaja v zaprtih prostorih. Ta doktorska dizertacija predstavlja novo metodo za določanje pozicije znotraj prostorov, z glavnim ciljem, da naslovi težave pri realni uporabi. Zato smo se osredotočili na razvoj metode z natančnostjo, ki je primerljiva z najsodobnejšimi metodami, hkrati pa je cilj zmanjšati kompleksnost implementacije in vzdrževanje za dolgoročno uporabnost. Predstavljena metoda je modelnega tipa in implementira prilagodljivo delovanje, zato ne zahteva nobenega človeškega posredovanja. Dizertacija podrobno razpravlja o temah dolgoročne stabilnosti WiFi signalov, o metodah, temelječih na sprejemanju in oddajanju signalov, prihodnjih standardih WiFi, primerljivosti sorodnih metod in arhitekturnih vplivih z ozirom na realno uporabnost. Naša metoda predstavljena v tej nalogi oceni prametre propagacije signala iz poznavanja pozicije dostopnih točk, arhitekturnega načrta z informacijami o predelnih stenah in s pomočjo opazovanja moči paketov, ki potujejo med dostopnimi točkami. Iz teh podatkov se propagacijski parametri definirani v modelu določijo v realnem času. Naprava, ki želi določiti pozicijo zajame informacijo o moči paketov, ki jih pošiljajo dostopne točke. Te meritve so uporabljene v algoritmu za določanje pozicije naprave, ki teče na strežniku. Predstavljena metoda je bila primarno razvita in evalvirana v enosobni in večsobni postavitvi pisarniškega okolja. Sposobnost metode, da se enostavno prilagodi vsakemu okolju, je poudarjena z evalvacijo v dveh okoljih – pisarniškem in stanovanjskem. Med obema evalvacijama nismo spremenili nobenega parametra metode, kar indicira njeno univerzalnost. V nadaljevanju predstavimo tudi evalvacijo metode v dolgem hodniku, ker je v raziskovalnem področju lokalizacije znotraj prostorov tako okolje pogosto uporabljeno. Evalvacija predlagane metode v pisarniškem okolju je rezultirala v povprečni napaki 2,63 m in 3,22 m za enosobno in večsobno postavitev. Druga evalvacija je bila opravljena v stanovanjskem okolju, za katerega nismo spreminjali metode ali njenih parametrov. Naša metoda je tekom evalvacije štirih neodvisnih setov meritev, od katerih je vsak sestavljen iz 17 lokalizacijskih točk, dosegla povprečno napako lokalizacije 2,65 m s standardno deviacijo 1,51 m. Visoka natančnost lokalizacije ob upoštevanju zapletenega in realističnega večsobnega tlorisa, ki vsebuje več vrst sten, realistično pohištvo in motnje signalov iz sosednjih stanovanj, dokazuje uporabnost metode v praksi. Natančnost je primerljiva z najsodobnejšimi metodami, medtem ko naša metoda zahteva veliko manj zapletene postopke namestitve in/ali strojne zahteve. V drugem delu teze posplošimo WiFi metodo, da lahko uporablja tudi druge frekvence poleg 2,4 GHz WiFi. Z definicijo fuzijskega algoritma, ki upošteva natančnost posameznih frekvenc, smo definirali MFAM metodo – večfrekvenčno prilagodljivo modelno metodo za določanje lokacije znotraj stavb (ang. multiple frequency adaptive model-based indoor localization method). MFAM metoda predstavlja eno prvih modelnih metod, ki lahko hkrati uporablja več frekvenc. MFAM metoda je bila evalvirana v stanovanjskem okolju na dveh frekvenčnih pasovih: 868 MHz in 2,4 GHz. Metoda je ohranila pozitivne lastnosti predlagane WiFi metode (tj. izključno modelni pristop, prilagodljivo delovanje, možnost široke uporabe na dosegljivi strojni opremi), hkrati pa rezultira v boljši natančnosti zaradi fuzije signalov več frekvenc. Uporaba več frekvenc je izboljšala povprečno napako iz 2,65 m pri uporabi WiFi na 2,16 m, s čimer se izboljša natančnost lokalizacije za 18%podobne izboljšave smo opazili tudi pri standardnemu odklonu. Čeprav je natančnost predstavljenih WiFi in MFAM metod primerljiva, če ne boljša, kot trenutno najsodobnejše metode, je eden najpomembnejših dosežkov našega dela uporabnost metode v realnih situacijah in njena dolgoročna stabilnost. Definicija naše metode zagotavlja, da bo natančnost metode ob času postavitve enaka kot dneve kasneje brez človeške interakcije

A Meta-Review of Indoor Positioning Systems

An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys

Adaptive indoor positioning system based on locating globally deployed WiFi signal sources

Recent trends in data driven applications have encouraged expanding location awareness to indoors. Various attributes driven by location data indoors require large scale deployment that could expand beyond specific venue to a city, country or even global coverage. Social media, assets or personnel tracking, marketing or advertising are examples of applications that heavily utilise location attributes. Various solutions suggest triangulation between WiFi access points to obtain location attribution indoors imitating the GPS accurate estimation through satellites constellations. However, locating signal sources deep indoors introduces various challenges that cannot be addressed via the traditional war-driving or war-walking methods. This research sets out to address the problem of locating WiFi signal sources deep indoors in unsupervised deployment, without previous training or calibration. To achieve this, we developed a grid approach to mitigate for none line of site (NLoS) conditions by clustering signal readings into multi-hypothesis Gaussians distributions. We have also employed hypothesis testing classification to estimate signal attenuation through unknown layouts to remove dependencies on indoor maps availability. Furthermore, we introduced novel methods for locating signal sources deep indoors and presented the concept of WiFi access point (WAP) temporal profiles as an adaptive radio-map with global coverage. Nevertheless, the primary contribution of this research appears in utilisation of data streaming, creation and maintenance of self-organising networks of WAPs through an adaptive deployment of mass-spring relaxation algorithm. In addition, complementary database utilisation components such as error estimation, position estimation and expanding to 3D have been discussed. To justify the outcome of this research, we present results for testing the proposed system on large scale dataset covering various indoor environments in different parts of the world. Finally, we propose scalable indoor positioning system based on received signal strength (RSSI) measurements of WiFi access points to resolve the indoor positioning challenge. To enable the adoption of the proposed solution to global scale, we deployed a piece of software on multitude of smartphone devices to collect data occasionally without the context of venue, environment or custom hardware. To conclude, this thesis provides learning for novel adaptive crowd-sourcing system that automatically deals with tolerance of imprecise data when locating signal sources

Fuzzy classifier ensembles for hierarchical WiFi-based semantic indoor localization

The number of applications for smartphones and tablets is growing exponentially in the last years. Many of these applications are supported by the so-called Location Based Services, which are expected to provide reliable real-time localization anytime and anywhere, no matter either outdoors or indoors. Even though outdoors world-wide localization has been successfully developed through the well-known Global Navigation Satellite System technology, its counterpart large-scale deployment indoors is not available yet. In previous work, we have already introduced a novel technology for indoor localization supported by a WiFi fingerprint approach. In this paper, we describe how to enhance such approach through the combination of hierarchical localization and fuzzy classifier ensembles. It has been tested and validated at the University of Edinburgh, yielding promising results.Ministerio de Economía y CompetitividadXunta de Galici

A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning

The weighted k-nearest neighbors (WkNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS). WkNN estimates the position of a target device by selecting k reference points (RPs) based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms

Continuous Space Estimation: Increasing WiFi-Based Indoor Localization Resolution without Increasing the Site-Survey Effort

Abstract Although much research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of system, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site surveying the environment, which is effort consuming. In this work, we propose an approach, based on support vector regression, to estimate the received signal strength at non-site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site survey effortThis work has been funded by TIN2014-56633-C3-3-R (ABS4SOWproject) from the Ministerio de Economía y Competitividad and the University of Alcalá Postdoctoral Research program (30400M000.541A.640.17)S