Context-aware Peer-to-Peer and Cooperative Positioning

Peer-to-peer and cooperative positioning represent one of the major evolutions for mass-market positioning, bringing together capabilities of Satellite Navigation and Communication Systems. It is well known that smartphones already provide user position leveraging both GNSS and information collected through the communication network (e.g., Assisted-GNSS). However, exploiting the exchange of information among close users can attain further benefits. In this paper, we deal with such an approach and show that sharing information on the environmental conditions that characterize the reception of satellite signals can be effectively exploited to improve the accuracy and availability of user positioning. This approach extends the positioning service to indoor environments and, in general, to any scenario where full visibility of the satellite constellation cannot be grante

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process

Wi-Fi Fingerprinting for Indoor Positioning

Wireless Fidelity (Wi-Fi) Fingerprinting is a remarkable approach developed by modern science to detect the user’s location efficiently. Today, the Global Positioning System (GPS) is used to keep track of our current location for outdoor positioning. In GPS technology, satellite signals cannot reach indoor environments as they are shielded from obstructions so that indoor environments with a lack of Line of Sight (LoS) do not provide enough satellite signal accuracy. Since indoor environments are very difficult to track, thus, a wide variety of techniques for dealing with them have been suggested. The best way to offer an indoor positioning service with the current technology is Wi-Fi since the most commercial infrastructure is well equipped with Wi-Fi routers. For indoor positioning systems (IPS), Wi-Fi fingerprinting approaches are being extremely popular. In this paper, all the approaches for Wi-Fi fingerprinting have been reviewed for indoor position localization. Related to Wi-Fi fingerprinting, most of the algorithms have been interpreted and the previous works of other researchers have been critically analyzed in this paper to get a clear view of the Wi-Fi fingerprinting process

Finnish permanent GNSS network FinnRef - evolution towards a versatile positioning service

The doctoral thesis is conducted in collaboration with Aalto University and Finnish Geospatial Research Institute.The National Land Survey of Finland maintains the FinnRef network of continuously operating GNSS reference stations (CORS). FinnRef is the basis of the EUREF-FIN reference frame in Finland. Continuous time series ensure an accurate link between global GNSS-based coordinates and the national reference frame. In a CORS network it is essential that coordinates and coordinate time series of the reference stations are up to date, accurate and free from biases. In this dissertation we introduce the development of FinnRef from a network of 13 GPS stations into a versatile modern positioning service. Both old and new FinnRef stations are explained and it is shown how a high quality CORS station should be established today. We were one of the first groups to show the annual periodicity of GNSS time series. In Finland, land uplift is of high importance since it changes the coordinates continuously. We compared our uplift rates to independent results (tide gauges, precise levelling, GPS results of the BIFROST group). Agreement was very good showing that GPS is a powerful tool for monitoring the land uplift in Finland. Using a baseline in Lithuania we tested an idea to validate GPS processing parameters against metrological ground truth. The length of the baseline is traceable to the definition of the metre with an uncertainty based on our calibrations. The test was successful and showed that for most accurate measurements individually calibrated antennas must be used.After the first renewal phase of FinnRef we showed that our national network of 20 stations can provide NRTK corrections of the same accuracy level as services having five times more stations. The challenge was the reliability of individual coordinate measurements but this can be overcome by proper use of repeated measurements. One of the most important results of this dissertation was that we showed the power of using metrological ground truth for validating GPS. The results of this dissertation will enable the creation of a dense GNSS based velocity field for intra-plate deformation models. This will improve the accuracy of transformations from measured GNSS coordinates to the national reference frame and make possible an accurate, reliable (semi)dynamic reference frame in Finland. It is also noteworthy that we showed how the FINPOS positioning service based on FinnRef data could give citizens direct access to the national EUREF-FIN reference frame. FinnRef could also be used as a backbone for GNSS corrections needed for intelligent traffic applications in Finland.Maanmittauslaitos kerää satelliittipaikannusjärjestelmien (GNSS) lähettämää dataa jatkuvasti rekisteröivien FinnRef-tukiasemien avulla. Maanlaajuinen FinnRef-verkko on Suomen EUREF-FIN -vertauskehyksen runko. Verkon keräämät pitkät aikasarjat mahdollistavat tarkkojen muunnosmallien johtamisen satelliittipaikannusjärjestelmien tuottamien maailmanlaajuisten koordinaattien ja kansallisen EUREF-FIN -järjestelmän välille. On oleellista, että pysyvien GNSS-asemien tuottamat koordinaatit ja koordinaattien aikasarjat ovat laadukkaita ja virheettömiä. Tässä väitöskirjassa esitetään Suomen pysyvän GNSS-verkon kehitys 13 aseman GPS-asemien verkosta kohti tiheämpää ja monipuolista paikannuspalvelua. Työssä esitellään alkuperäisen FinnRef-verkon ja uudistetun verkon rakenne, toimintaperiaatteet ja uusin tieto siitä kuinka asemat tulee tänä päivänä perustaa. Olimme yksi ensimmäisistä tutkimusryhmistä, jotka raportoivat GPS-aikasarjoissa selkeää vuotuista periodisuutta. Suomessa eräs koordinaatteja jatkuvasti muuttava ilmiö on maankohoaminen. Vertasimme omia GPS:llä saatuja maankohoamisarvoja riippumattomiin tuloksiin (toistetut tarkkavaaitukset, mareografihavainnot ja kansainvälisen BIFROST-tutkimusryhmän eri metodilla tekemä GPS-analyysi). Yhteensopivuus oli hyvä ja osoitti GPS:n olevan tehokas työkalu myös maankuoren pystyliikkeiden seurantaan. Testasimme Liettuassa olevalla perusviivalla mahdollisuutta validoida GPS-laskentaparametreja metrologisesti luotettavaan referenssimittaukseen. Mittauksen epävarmuusketju on tekemämme kalibroinnin perusteella jäljitettävissä metrin määritelmään. Testi osoittautui menestykselliseksi ja osoitimme, että tarkimpiin mahdollisiin mittauksiin jokainen GPS-antenni tulee kalibroida yksilönä. Ensimmäisen FinnRef-verkon uudistuksen jälkeen osoitimme, että valtakunnallisella 20 tukiaseman GNSS verkolla voidaan päästä samalle tarkkuustasolle kuin viisi kertaa enemmän asemia olevissa palveluissa. Haasteeksi tulee yksittäisten mittausten luotettavuus, joka voidaan ratkaista toistomittauksin. Väitöskirjan tärkeimpiä tuloksia oli osoittaa metrologisen lähestymistavan hyväksikäytön hyödyllisyys validoitaessa GNSS-laskentaparametreja. Tulokset mahdollistavat entistä tarkemman GNSS-tukiasemien dataan perustuvan maankuoren liikemallien luomisen, joka tarkentaa kansainvälisten ja kansallisen koordinaattijärjestelmien välistä muunnosta sekä aikariippuvan koordinaattijärjestelmän. Osoitimme myös, kuinka FinnRef-verkkoon perustuva FINPOS-paikannuspalvelu mahdollistaisi kansalaisille saumattoman pääsyn kansalliseen EUREF-FIN -järjestelmään. FinnRef-verkkoa voitaisiin hyödyntää myös runkoverkkona, joka voi osaltaan auttaa parantamaan älyliikenteen paikannuksen tarkkuutta

Infrastructure-Less Indoor Localization Using the Microphone, Magnetometer and Light Sensor of a Smartphone

In this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of informationIn this paper, we present the development of an infrastructure-less indoor location system (ILS), which relies on the use of a microphone, a magnetometer and a light sensor of a smartphone, all three of which are essentially passive sensors, relying on signals available practically in any building in the world, no matter how developed the region is. In our work, we merge the information from those sensors to estimate the user’s location in an indoor environment. A multivariate model is applied to find the user’s location, and we evaluate the quality of the resulting model in terms of sensitivity and specificity. Our experiments were carried out in an office environment during summer and winter, to take into account changes in light patterns, as well as changes in the Earth’s magnetic field irregularities. The experimental results clearly show the benefits of using the information fusion of multiple sensors when contrasted with the use of a single source of informatio

Context Awareness for Navigation Applications

This thesis examines the topic of context awareness for navigation applications and asks the question, “What are the benefits and constraints of introducing context awareness in navigation?” Context awareness can be defined as a computer’s ability to understand the situation or context in which it is operating. In particular, we are interested in how context awareness can be used to understand the navigation needs of people using mobile computers, such as smartphones, but context awareness can also benefit other types of navigation users, such as maritime navigators. There are countless other potential applications of context awareness, but this thesis focuses on applications related to navigation. For example, if a smartphone-based navigation system can understand when a user is walking, driving a car, or riding a train, then it can adapt its navigation algorithms to improve positioning performance. We argue that the primary set of tools available for generating context awareness is machine learning. Machine learning is, in fact, a collection of many different algorithms and techniques for developing “computer systems that automatically improve their performance through experience” [1]. This thesis examines systematically the ability of existing algorithms from machine learning to endow computing systems with context awareness. Specifically, we apply machine learning techniques to tackle three different tasks related to context awareness and having applications in the field of navigation: (1) to recognize the activity of a smartphone user in an indoor office environment, (2) to recognize the mode of motion that a smartphone user is undergoing outdoors, and (3) to determine the optimal path of a ship traveling through ice-covered waters. The diversity of these tasks was chosen intentionally to demonstrate the breadth of problems encompassed by the topic of context awareness. During the course of studying context awareness, we adopted two conceptual “frameworks,” which we find useful for the purpose of solidifying the abstract concepts of context and context awareness. The first such framework is based strongly on the writings of a rhetorician from Hellenistic Greece, Hermagoras of Temnos, who defined seven elements of “circumstance”. We adopt these seven elements to describe contextual information. The second framework, which we dub the “context pyramid” describes the processing of raw sensor data into contextual information in terms of six different levels. At the top of the pyramid is “rich context”, where the information is expressed in prose, and the goal for the computer is to mimic the way that a human would describe a situation. We are still a long way off from computers being able to match a human’s ability to understand and describe context, but this thesis improves the state-of-the-art in context awareness for navigation applications. For some particular tasks, machine learning has succeeded in outperforming humans, and in the future there are likely to be tasks in navigation where computers outperform humans. One example might be the route optimization task described above. This is an example of a task where many different types of information must be fused in non-obvious ways, and it may be that computer algorithms can find better routes through ice-covered waters than even well-trained human navigators. This thesis provides only preliminary evidence of this possibility, and future work is needed to further develop the techniques outlined here. The same can be said of the other two navigation-related tasks examined in this thesis

An Indoor Positioning System Based on Wearables for Ambient-Assisted Living

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an indoor positioning system for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.This work has been partially funded by the Spanish Ministry of Economy and Competitiveness through the “Proyectos I + D Excelencia” programme (TIN2015-70202-P) and the “Redes de Excelencia” programme (TEC2015-71426-REDT), and from the Regional Government of Valencia (‘Proyectos de I + D para Grupos de Investigación Emergentes’ GV/2016/159). Special thanks to Víctor, Maricarmen, Inma and Daniel who lent their houses for performing the experiments

Multisensor navigation systems: a remedy for GNSS vulnerabilities?

Space-based positioning, navigation, and timing (PNT) technologies, such as the global navigation satellite systems (GNSS) provide position, velocity, and timing information to an unlimited number of users around the world. In recent years, PNT information has become increasingly critical to the security, safety, and prosperity of the World's population, and is now widely recognized as an essential element of the global information infrastructure. Due to its vulnerabilities and line-of-sight requirements, GNSS alone is unable to provide PNT with the required levels of integrity, accuracy, continuity, and reliability. A multisensor navigation approach offers an effective augmentation in GNSS-challenged environments that holds a promise of delivering robust and resilient PNT. Traditionally, sensors such as inertial measurement units (IMUs), barometers, magnetometers, odometers, and digital compasses, have been used. However, recent trends have largely focused on image-based, terrain-based and collaborative navigation to recover the user location. This paper offers a review of the technological advances that have taken place in PNT over the last two decades, and discusses various hybridizations of multisensory systems, building upon the fundamental GNSS/IMU integration. The most important conclusion of this study is that in order to meet the challenging goals of delivering continuous, accurate and robust PNT to the ever-growing numbers of users, the hybridization of a suite of different PNT solutions is required

Robustness, Security and Privacy in Location-Based Services for Future IoT : A Survey

Internet of Things (IoT) connects sensing devices to the Internet for the purpose of exchanging information. Location information is one of the most crucial pieces of information required to achieve intelligent and context-aware IoT systems. Recently, positioning and localization functions have been realized in a large amount of IoT systems. However, security and privacy threats related to positioning in IoT have not been sufficiently addressed so far. In this paper, we survey solutions for improving the robustness, security, and privacy of location-based services in IoT systems. First, we provide an in-depth evaluation of the threats and solutions related to both global navigation satellite system (GNSS) and non-GNSS-based solutions. Second, we describe certain cryptographic solutions for security and privacy of positioning and location-based services in IoT. Finally, we discuss the state-of-the-art of policy regulations regarding security of positioning solutions and legal instruments to location data privacy in detail. This survey paper addresses a broad range of security and privacy aspects in IoT-based positioning and localization from both technical and legal points of view and aims to give insight and recommendations for future IoT systems providing more robust, secure, and privacy-preserving location-based services.Peer reviewe

Investigations of geoid models in Finland - Towards GNSS-related height system

Parts / Osajulkaisut: Publication 1: Saari, T., Poutanen, M., Saaranen, V., Kaartinen, H., Kukko, A., Nyberg, S. (2015). Height Determination Techniques for the Next National Height System of Finland - A Case Study. Geodesy and Cartography. 41(4):145–155. DOI: 10.3846/20296991.2015.1120387 Publication 2: Saari, T., Bilker-Koivula, M. (2015). Evaluation of GOCE-based Global Geoid Models in Finnish Territory. Newton's Bulletin. 5:25–36. https://www.isgeoid.polimi.it/Newton/Newton_5/04_Saari_25_36.pdf Publication 3: Saari, T., Bilker-Koivula, M. (2017). Applying the GOCE-based GGMs for the quasi-geoid modelling of Finland. Journal of Applied Geodesy. 12(1):15–27. DOI: 10.1515/jag-2017-0020 Publication 4: Saari, T., Bilker-Koivula, M., Koivula, H., Nordman, M., Häkli, P., Lahtinen, S. (2021). Validating Geoid Models with Marine GNSS Measurements, Sea Surface Models, and Additional Gravity Observations in the Gulf of Finland. Marine Geodesy. 44(3):196–214. DOI:10.1080/01490419.2021.1889727In Fennoscandia, heights and their relations between each other are in constant change due to the post-glacial rebound, so the national height system needs to be updated occasionally. Traditionally, the update has been done with a method known as precise levelling, which is accurate but considered slow, laborious, and expensive. This dissertation studied the modern, mainly Global Navigation Satellite System (GNSS) -based, height determination techniques that could replace precise levelling as the method for the next national height system of Finland. GNSS-based techniques provide the height component relative to a reference ellipsoid, which is a mathematical surface and therefore lacks a physical connection to the Earth – i.e. have no information on the direction of water flow. Here, we need a (quasi-)geoid model to tie the ellipsoidal heights to the surface of the Earth and to national height systems. The (quasi-)geoid model's accuracy is therefore crucial with GNSS/geoid techniques. The presented case study gave us knowledge from several techniques. The static GNSS proved the most promising, as the result was close to the one from precise levelling. However, due to the closeness of the evaluation points, the relative error of the geoid between them is negligible in practice, which is not the case in a nationwide network. The national quasi-geoid model of Finland, FIN2005N00, was created nearly two decades ago. The gravity satellites, Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) and Gravity Recovery and Climate Experiment (GRACE), have since measured the Earth's gravitational field in unprecedented detail. At the time, we investigated all the published GOCE and GRACE global gravitational models (GGM) in Finland. We learned that the best models already performed at the same level or better than the pre-GOCE era high-resolution models. The most suitable model, DIR5, was chosen as a background model with the high-resolution EIGEN-6C4 in the quasi-geoid modelling of Finland. The new and present quasi-geoid models were evaluated on land and in sea areas. A significant improvement was achieved over the present models.A marine GNSS/gravity campaign was performed in the Gulf of Finland to improve the accuracy and validation of a quasi-geoid model in sea areas. Geoid height differences of up to 15 cm were found with the new gravity data included in the quasi-geoid modelling. The result was confirmed in the evaluation with marine GNSS measurements in combination with sea surface models. This dissertation's results will be important for producing the next national quasi-geoid model of Finland. Additionally, the knowledge obtained from the GNSS/geoid method will be beneficial for the decision making of the chosen method for the next national height system of Finland.Jääkauden jälkeinen maankohoaminen muuttaa korkeuksia sekä niiden keskinäisiä suhteita Fenno-skandian alueella. Tästä johtuen, Suomen kansallinen korkeusjärjestelmä tulee päivittää sopivin välein. Korkeusjärjestelmät ovat perinteisesti perustuneet tarkkavaaitukseen, mikä on tarkka, mutta hidas, työläs sekä kallis menetelmä. Tässä väitöskirjassa tutkittiin moderneja, Global Navigation Satellite System (GNSS) -pohjaisia, korkeudenmääritysmenetelmiä, joilla voitaisiin korvata tarkkavaaitus Suomen seuraavaa kansallista korkeusjärjestelmää määritettäessä. GNSS-menetelmillä saadaan korkeus vertausellipsoidista, mikä on matemaattinen malli Maan muodosta. Ellipsoidikorkeuksilla ei täten ole sidosta fyysiseen Maan pintaan, eivätkä sisällä tietoa mihin suuntaan vesi virtaisi. Jotta ellipsoidikorkeuksille saadaan fyysinen merkitys, ja niitä voitaisiin käyttää kansallisissa korkeusjärjestelmissä, tarvitaan (kvasi)geoidimalli. GNSS-menetelmien arvioimisessa (kvasi)geoidimallien tarkkuuksilla on erittäin merkittävä rooli. Esitetyssä kenttäkokeessa vertailtiin korkeudenmääritysmenetelmiä, joista staattinen GNSS-mittaus osoittautui lupaavimmaksi. Menetelmällä on useita käytännön etuja tarkkavaaitukseen nähden, minkä lisäksi tulokset olivat hyvin lähellä toisiaan. Kokeessa kvasigeoidimallin sisältämät epävarmuudet kuitenkin mitätöityivät, johtuen havaintopisteiden läheisyydestä. Mallien epävarmuudet täytyisi huomioida laajemmissa, erityisesti valtakunnanlaajuisissa kampanjoissa. Suomen virallisen kvasigeoidimallin, FIN2005N00, julkaisemisen jälkeen painovoimasatelliitit Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) ja Gravity Recovery and Climate Experiment (GRACE) ovat mitanneet Maan painovoimakenttää ennennäkemättömällä tarkkuudella. Vertailimme kaikki tutkimuksemme aikaan saatavilla olevat GOCE- sekä GRACE-painovoimamallit Suomen alueella, missä tarkimmat mallit olivat jopa GOCE-satelliittia edeltäviä korkearesoluutioisia malleja tarkempia. Sopivin malli, DIR5, valittiin Suomen kvasigeoidi-laskentaan globaaliksi taustamalliksi, yhdessä EIGEN-6C4 kanssa. Kvasigeoidimallit arvioitiin Suomessa maa- ja merialueilla, joissa tutkimuksen uudet mallit olivat nykyisiä malleja tarkempia. Viimeisessä tutkimuksessa mittasimme Suomenlahdella GNSS-/painovoimakampanjan, jolla pyrittiin parantamaan sekä arvioimaan kvasigeoidimalleja merialueilla. Kampanjan painovoima-mittauksilla oli peräti 15 cm vaikutus kvasigeoidimalliin, mikä oli havaittavissa myös menetelmällä, jolla malleja arvioitiin GNSS-mittauksilla yhdessä merenpintamallien kanssa. Tämän väitöskirjan tulokset ovat merkittäviä Suomen seuraavan kansallisen kvasigeoidimallin luomiselle. Lisäksi, GNSS/geoidi -menetelmästä saatua tietoa tullaan hyödyntämään päätöksenteossa, missä valitaan menetelmä Suomen tulevan kansallisen korkeusjärjestelmän luomiselle