Human Crowdsourcing Data for Indoor Location Applied to Ambient Assisted Living Scenarios

In the last decades, the rise of life expectancy has accelerated the demand for new technological solutions to provide a longer life with improved quality. One of the major areas of the Ambient Assisted Living aims to monitor the elderly location indoors. For this purpose, indoor positioning systems are valuable tools and can be classified depending on the need of a supporting infrastructure. Infrastructure-based systems require the investment on expensive equipment and existing infrastructure-free systems, although rely on the pervasively available characteristics of the buildings, present some limitations regarding the extensive process of acquiring and maintaining fingerprints, the maps that store the environmental characteristics to be used in the localisation phase. These problems hinder indoor positioning systems to be deployed in most scenarios. To overcome these limitations, an algorithm for the automatic construction of indoor floor plans and environmental fingerprints is proposed. With the use of crowdsourcing techniques, where the extensiveness of a task is reduced with the help of a large undefined group of users, the algorithm relies on the combination ofmultiple sources of information, collected in a non-annotated way by common smartphones. The crowdsourced data is composed by inertial sensors, responsible for estimating the users’ trajectories, Wi-Fi radio and magnetic field signals. Wi-Fi radio data is used to cluster the trajectories into smaller groups, each corresponding to specific areas of the building. Distance metrics applied to magnetic field signals are used to identify geomagnetic similarities between different users’ trajectories. The building’s floor plan is then automatically created, which results in fingerprints labelled with physical locations. Experimental results show that the proposed algorithm achieved comparable floor plan and fingerprints to those acquired manually, allowing the conclusion that is possible to automate the setup process of infrastructure-free systems. With these results, this solution can be applied in any fingerprinting-based indoor positioning system

Recurrent Neural Networks For Accurate RSSI Indoor Localization

This paper proposes recurrent neuron networks (RNNs) for a fingerprinting indoor localization using WiFi. Instead of locating user's position one at a time as in the cases of conventional algorithms, our RNN solution aims at trajectory positioning and takes into account the relation among the received signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a weighted average filter is proposed for both input RSSI data and sequential output locations to enhance the accuracy among the temporal fluctuations of RSSI. The results using different types of RNN including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of $0.75$ m with $80\%$ of the errors under $1$ m, which outperforms the conventional KNN algorithms and probabilistic algorithms by approximately $30\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, recurrent neuron network (RNN), long shortterm memory (LSTM), fingerprint-based localizatio

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

WIFI BASED INDOOR POSITIONING - A MACHINE LEARNING APPROACH

Navigation has become much easier these days mainly due to advancement in satellite technology. The current navigation systems provide better positioning accuracy but are limited to outdoors. When it comes to the indoor spaces such as airports, shopping malls, hospitals or office buildings, to name a few, it will be challenging to get good positioning accuracy with satellite signals due to thick walls and roofs as obstacles. This gap led to a whole new area of research in the field of indoor positioning. Many researches have been conducting experiments on different technologies and successful outcomes have beenseen. Each technology providing indoor positioning capability has its own limitations. In this thesis, different radio frequency (RF) and non-radio frequency (Non-RF) technologies are discussed but focus is set on Wi-Fi for indoor positioning. A demo indoor positioning app is developed for the Technobothnia building at the University of Vaasa premises. This building is already equipped with Wi-Fi infrastructure. A floor plan of the building, radio maps and a fingerprinting database with Wi-Fi signal strength measurements is created with help of tools from HERE technology. The app provides real-time positioning and routing as a future visitor tool. With the exceeding amounts of available data, one of the highly popular fields is applying Machine Learning (ML) to data. It can be applied in many disciplines from medicine to space. In ML, algorithms learn from the data and make predictions. Due to the significant growth in various sensor technologies and computational power, large amounts of data can be stored and processed. Here, the ML approach is also taken to the indoor positioning challenge. An open-source Wi-Fi fingerprinting dataset is obtained from Tampere University and ML algorithms are applied on it for performing indoor positioning. Algorithms are trained with received signal strength (RSS) values with their respective reference coordinates and the user location can be predicted. The thesis provides a performance analysis of different algorithms suitable for future mobile implementations

Crowdsourcing-Based Fingerprinting for Indoor Location in Multi-Storey Buildings

POCI-01-0247-FEDER-033479The number of available indoor location solutions has been growing, however with insufficient precision, high implementation costs or scalability limitations. As fingerprinting-based methods rely on ubiquitous information in buildings, the need for additional infrastructure is discarded. Still, the time-consuming manual process to acquire fingerprints limits their applicability in most scenarios. This paper proposes an algorithm for the automatic construction of environmental fingerprints on multi-storey buildings, leveraging the information sources available in each scenario. It relies on unlabelled crowdsourced data from users’ smartphones. With only the floor plans as input, a demand for most applications, we apply a multimodal approach that joins inertial data, local magnetic field andWi-Fi signals to construct highly accurate fingerprints. Precise movement estimation is achieved regardless of smartphone usage through Deep Neural Networks, and the transition between floors detected from barometric data. Users’ trajectories obtained with Pedestrian Dead Reckoning techniques are partitioned into clusters with Wi-Fi measurements. Straight sections from the same cluster are then compared with subsequence Dynamic Time Warping to search for similarities. From the identified overlapping sections, a particle filter fits each trajectory into the building’s floor plans. From all successfully mapped routes, fingerprints labelled with physical locations are finally obtained. Experimental results from an office and a university building show that this solution constructs comparable fingerprints to those acquired manually, thus providing a useful tool for fingerprinting-based solutions automatic setup.publishersversionpublishe

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

Sisäpaikannus: Teknologiat ja käyttötapaukset vähittäiskaupan alalla

Indoor positioning systems (IPS) are required in buildings to offer the possibility to position people and assets indoors, as the widely utilized GPS signal cannot penetrate through walls. IPSs are already implemented in many indoor environments. Several indoor positioning technologies exist, but none of them is clearly a dominant technology over the others. Consequently, this study identifies the different kinds of indoor positioning technologies and methods as well as the use cases they are used in. For this purpose, six companies using or developing indoor positioning systems were interviewed. The interviews were held in person, and they were 60-minute long semi-structured interviews with a set of questions in Appendix 1. In addition, two companies interested in indoor positioning, and that are working with retail were interviewed in 30-minute semi-structured interviews with questions in Appendix 2. Indoor positioning is employed in the interviewed companies to help users to navigate in public spaces; raise employee satisfaction in an office; improve customer service and satisfaction in malls, stores, and restaurants and develop processes and safety in warehouses. These different use cases have distinctive specifications and needs for indoor positioning, and thus, there is not a simple solution as to which technology is the right choice for a particular use case. Nevertheless, three points affecting the choice of indoor positioning technology were concluded from the interviews: 1) the accuracy of a technology, 2) whether the positioning happens through a tag or a mobile device, and 3) if positioning infrastructure, such as anchor nodes, can be installed in the building. Finally, based on the interviews, a suggested model for an indoor positioning system for a retail company is presented in a form of a Value Network Configuration.Sisäpaikannusjärjestelmiä tarvitaan rakennuksissa, jotta ihmisiä ja tavaroita voidaan paikantaa sisätiloissa, sillä ulkona yleisesti käytetty GPS signaali ei pysty läpäisemään rakennusten seiniä. Vaikka sisäpaikannusta käytetäänkin jo useissa eri sisätiloissa ja useita eri sisäpaikannusteknologioita on olemassa, mikään niistä ei ole selvästi hallitseva teknologia. Tässä tutkimuksessa tunnistetaan eri sisäpaikannusteknologiat ja –tekniikat kuten myös niitä hyödyntävät käyttötapaukset. Tätä varten haastateltiin kuutta eri yritystä, jotka käyttävät tai tarjoavat sisäpaikannusjärjestelmiä. Haastattelut olivat puolistrukturoituja, kestivät 60 minuuttia ja ne pidettiin kasvotusten. Lisäksi haastateltiin 30 minuutin puolistrukturoiduissa haastatteluissa kahta kaupan alaan liittyvää yritystä, jotka ovat kiinnostuneita sisäpaikannuksesta. Haastattelukysymykset ovat liitteissä 1 ja 2. Sisäpaikannusta käytetään haastatelluissa yrityksissä käyttäjien navigoinnin helpottamiseksi julkisissa tiloissa, työntekijöiden tyytyväisyyden kasvattamiseen toimistossa, asiakaspalvelun ja asiakkaiden tyytyväisyyden parantamiseen ostoskeskuksissa, kaupoissa ja ravintoloissa sekä prosessien ja turvallisuuden kehittämiseen varastoissa. Näillä eri käyttötapauksilla on hyvin erilaiset vaatimukset ja tarpeet sisäpaikannukselle, joten ei ole olemassa vain yhtä hyvää teknologista ratkaisua tietylle käyttötapaukselle. Haastatteluista oli kuitenkin mahdollista muodostaa kolme sisäpaikannusteknologian valintaan vaikuttavaa asiaa: 1) sisäpaikannusteknologian tarkkuus, 2) tapahtuuko paikannus mobiililaitteen vai käyttäjän kantaman tunnisteen kautta ja 3) voiko paikannusjärjestelmän tukiasemia asentaa rakennukseen. Lopuksi esitellään ehdotelma sisäpaikannusmallista arvoverkkokonfiguraatiolla (Value Network Configuration) vähittäiskaupan alan yritykselle haastatteluiden perusteella

An infrastructure-free magnetic-based indoor positioning system with deep learning

POCI-01-0247-FEDER-033479Infrastructure-free Indoor Positioning Systems (IPS) are becoming popular due to their scalability and a wide range of applications. Such systems often rely on deployed Wi-Fi networks. However, their usability may be compromised, either due to scanning restrictions from recent Android versions or the proliferation of 5G technology. This raises the need for new infrastructure-free IPS independent of Wi-Fi networks. In this paper, we propose the use of magnetic field data for IPS, through Deep Neural Networks (DNN). Firstly, a dataset of human indoor trajectories was collected with different smartphones. Afterwards, a magnetic fingerprint was constructed and relevant features were extracted to train a DNN that returns a probability map of a user’s location. Finally, two postprocessing methods were applied to obtain the most probable location regions. We asserted the performance of our solution against a test dataset, which produced a Success Rate of around 80%. We believe that these results are competitive for an IPS based on a single sensing source. Moreover, the magnetic field can be used as an additional information layer to increase the robustness and redundancy of current multi-source IPS.publishersversionpublishe

Minet Magnetic Indoor Localization

Indoor localization is a modern problem of computer science that has no unified solution, as there are significant trade-offs involved with every technique. Magnetic localization, though less popular than WiFi signal based localization, is a sub-field that is rooted in infrastructure-free design, which can allow universal setup. Magnetic localization is also often paired with probabilistic programming, which provides a powerful method of estimation, given a limited understanding of the environment. This thesis presents Minet, which is a particle filter based localization system using the Earth\u27s geomagnetic field. It explores the novel idea of state space limitation as a method of optimizing a particle filter, by limiting the scope of possibilities the filter has to predict. Minet is also built as a distributed model, which can be easily modified to integrate new technologies. Minet showed promising results, but ultimately fell short of its accuracy goal. Minet had some inconsistencies that led to these accuracy issues, but these issues have been diagnosed and can be fixed in future updates. Finally, potential improvements of Minet\u27s base components are discussed, along with how different technologies such as a Deep Learning model can be implemented to improve performance