A Soft Range Limited K-Nearest Neighbours Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization, K-nearest neighbor (KNN), fingerprint-based localizatio

Sisätilapaikannusmenetelmä huonetarkkuuden parantamiseksi käyttäen staattisia radiomajakoita

Demand for indoor positioning applications has been growing lately. Indoor positioning is used for example in hospitals for patient tracking, and in airports for finding correct gates. Requirements in indoor positioning have become more strict with demands for a higher accuracy. This thesis presents a method for improving the room-level accuracy of a positioning system by using static beacons. As a static beacon, Bluetooth low energy modules will be used to test how much they can improve room-level accuracy on top of an existing positioning system. First, base technologies used in indoor positioning are reviewed. These include but not limited to: WLAN, Bluetooth and Ultra-wideband. Then, general indoor positioning are reviewed. After that, Ekahau positioning system will be introduced, as it is used as the base positioning system in the proposed method. The Ekahau positioning system applies WiFi networks and a fingerprinting method. Next, tests and their results in the thesis are shown. In the first test, a received signal strength of a static emulated WiFi AP is measured indoors to study radio signal behaviour indoors and to investigate whether the proposed method could improve the room-level accuracy. Then in the second test, the proposed method is presented and tested with the Ekahau positioning system. The results showed that it is possible to improve the room-level accuracy by using static beacons.Sisätilapaikannusta hyödyntäville sovelluksille on ollut nouseva kysyntä. Tällä hetkellä sisätilapaikannusta käytetään muun muassa sairaaloissa muistiongelmista kärsivien potilaiden seuraamiseen, ja lentokentällä oikean lentoportin löytämiseen. Sisätilapaikannussovellusten vaatimukset ovat samalla kasvaneet. Tarkkuudessa, ja eritoten huonetarkkuudessa on ollut parantamisen varaa. Tässä työssä esitetään menetelmä huonetarkkuuden parantamiseksi käyttämällä staattisia radiomajakoita. Radiomajakkana työn testeissä käytetään Bluetooth low energy -moduulia. Bluetooth low energy -moduulia testataan Ekahau-paikannusjärjestelmän kanssa, nähdäksemme parantuuko huonetarkkuus moduulia käytettäessä. Esiksi työssä esitetään teknologioita, joiden päälle monet sisätilapaikannusmenetelmät rakennetaan. Näitä ovat muun muassa WLAN, Bluetooth ja Ultrawideband. Sen jälkeen käsitellään sisätilapaikannusmenetelmiä, jotka käyttävät edellä mainittuja teknologioita. Sitten esitellään Ekahau-paikannusjärjestelmä, jota käytetään tulevissa testeissä. Ekahau-paikannusjärjestelmä käyttää hyödykseen WLAN:ia, ja perustuu menetelmään nimeltä Fingerprinting. Lopuksi esitellään työssä tehdyt testit. Ensimmäisessä testissä mitattiin emuloidun WiFi -tukiaseman lähettämien signaalien voimakkuutta sisätiloissa. Testin tarkoituksena oli tutkia radiosignaalien käyttäytymistä sisätiloissa ja sitä, kuinka hyvät mahdollisuudet työssä esitetyllä menetelmällä voi olla parantaa huonetarkkuutta. Testin tulokset olivat positiivisia. Toisessa testissä testattiin huonetarkkuutta käyttäen Bluetooth low energy moduulia Ekahaun paikannusjärjestelmän kanssa. Testiskenaarion tuloksena oli, että huonetarkkuutta onnistuttiin parantamaan käyttämällä staattisia radiomajakoita

Distributed localisation algorithm for wireless ad hoc networks of moving nodes

Existing ad hoc network localisation solutions rely either on external location references or network-wide exchange of information and centralised processing and computation of location estimates. Without these, nodes are not able to estimate the relative locations of other nodes within their communication range. This thesis defines a new distributed localisation algorithm for ad hoc networks of moving nodes. The Relative Neighbour Localisation (RNL) algorithm works without any external localisation signal or systems and does not assume centralised information processing. The idea behind the location estimates produced by the RNL algorithm is the relationship between the relative locations of two nodes, their mobility parameters and the signal strengths measured between them. The proposed algorithm makes use of the data available to each node to produce a location estimate. The signal strength each node is capable of measuring is used as one algorithm input. The other input is the velocity vector of the neighbouring node, composed of its speed and direction of movement, which each node is assumed to periodically broadcast. The relationship between the signal strength and the mobility parameters on one, and the relative location on the other side can be analytically formulated in an ideal case. The limitations of a realistic scenario complicate this relationship, making it very difficult to formulate analytically. An empirical approach is thus used. The angle and the distance estimates are individually computed, together forming a two-dimensional location estimate. The performance of the algorithm was analysed in detail using simulation, showing a median estimate error of under 10m, and its application was tested through design and evaluation of a distributed sensing coverage algorithm, showing RNL location estimates can provide 90% of the coverage achievable with true locations being known

Sensor Fused Indoor Positioning Using Dual Band WiFi Signal Measurements

A ubiquitous and accurate positioning system for mobile devices is of great importance both to business and research due to the large number of applications and services it enables. In most outdoor environments this problem was solved by the introduction of the Global Positioning System (GPS). In indoor or suburban areas however, the GPS signals are often too weak to enable a reliable position estimate. Instead, other techniques must be utilized to provide accurate positioning. One of these is trilateration based on WiFi signal strengths. This is an auspicious technology to use partly because of the large number of access points (APs) in our everyday environment, and partly due to the possibility of measuring signal strength with a normal smartphone. The technique is further enabled by the move to include transmitters at 2.4 as well as 5 GHz in modern APs, providing a better basis for accurate position estimations. Furthermore, the motion sensors present in today’s smartphones are accurate enough to provide a short-time estimate of the user’s movement with high accuracy. In this thesis, both of these technologies are used to develop an accurate method for indoor positioning, and the contributions can be summed up into two points. The first contribution is an investigation of the behavior of two WiFi frequencies, 2.4 and 5 GHz, where their time dependent noise is proven to be almost uncorrelated with each other. This is then exploited to develop aWiFi-only trilateration algorithm by the use of a particle filter (PF), where the only restriction is that the locations of the APs need to be known. The second contribution is adding an accelerometer and a gyroscope to the algorithm, to provide a more accurate estimation. A step counter is developed using the accelerometer, and the gyroscope detects changes in heading while the WiFi signal strengths give information about the position. This makes it possible to alongside the position also estimate both heading and step length, while still keeping the only restriction of knowing the AP locations. The resulting algorithm produces position estimates with a mean error less than two meters for a specific use case, and around three meters when a more lenient user behavior is allowed

Who wrote this scientific text?

The IEEE bibliographic database contains a number of proven duplications with indication of the original paper(s) copied. This corpus is used to test a method for the detection of hidden intertextuality (commonly named "plagiarism"). The intertextual distance, combined with the sliding window and with various classification techniques, identifies these duplications with a very low risk of error. These experiments also show that several factors blur the identity of the scientific author, including variable group authorship and the high levels of intertextuality accepted, and sometimes desired, in scientific papers on the same topic

An Overview on IEEE 802.11bf: WLAN Sensing

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirements for emerging sensing applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its formation and standardization timeline. Next, we discuss the WLAN sensing use cases with the corresponding key performance indicator (KPI) requirements. After reviewing previous WLAN sensing research based on communication-oriented WLAN standards, we identify their limitations and underscore the practical need for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the WLAN sensing framework and procedure used for measurement acquisition, by considering both sensing at sub-7GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively, and address their shared features, similarities, and differences. In addition, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, as well as quantization and compression techniques. We also describe the methodologies and the channel modeling used by the IEEE 802.11bf TG for evaluation. Finally, we discuss the challenges and future research directions to motivate more research endeavors towards this field in details.Comment: 31 pages, 25 figures, this is a significant updated version of arXiv:2207.0485

Wi-Fi based people tracking in challenging environments

People tracking is a key building block in many applications such as abnormal activity detection, gesture recognition, and elderly persons monitoring. Video-based systems have many limitations making them ineffective in many situations. Wi-Fi provides an easily accessible source of opportunity for people tracking that does not have the limitations of video-based systems. The system will detect, localise, and track people, based on the available Wi-Fi signals that are reflected from their bodies. Wi-Fi based systems still need to address some challenges in order to be able to operate in challenging environments. Some of these challenges include the detection of the weak signal, the detection of abrupt people motion, and the presence of multipath propagation. In this thesis, these three main challenges will be addressed. Firstly, a weak signal detection method that uses the changes in the signals that are reflected from static objects, to improve the detection probability of weak signals that are reflected from the person’s body. Then, a deep learning based Wi-Fi localisation technique is proposed that significantly improves the runtime and the accuracy in comparison with existing techniques. After that, a quantum mechanics inspired tracking method is proposed to address the abrupt motion problem. The proposed method uses some interesting phenomena in the quantum world, where the person is allowed to exist at multiple positions simultaneously. The results show a significant improvement in reducing the tracking error and in reducing the tracking delay

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

Sensory fusion of UBW-TOF-based location systems for mobile robotics

With the increasing need for mobile robots in industrial applications, real-time location systems, which is a crucial point in these applications, has attracted attention from many researchers around the world. Thus, robot location is the process of determining the robot position and orientation in its environment. Location systems using Ultra-WideBand (UWB) have been widely used in complex urban and indoor environments. Consequently, a moving UWB tag can be located by measuring the distances to fixed UWB anchors whose positions are known in advance. The difficulty of this approach remains in the fact that the measurements are not perfect. There will always be some noise in the measurements, and because of this, position determination could contain some errors that may result in decreased accuracy. In this work, the Pozyx performance, a low-cost Ultra-WideBand (UWB) Time-of-flight (TOF) technology solution, is studied and implemented on a mobile robot, through a beacon-based location scheme. In order to reduce the impact of measurement noise and system disturbances, the readings of odometry, Pozyx measures and the information of the lines of a known navigation path are fused to improve the estimated location of the mobile robot. Therefore, the goal of this integration is to improve the accuracy of location for indoor autonomous robots. Firstly, was studied the characterisation of the Pozyx measurement error among several test conditions. Then, an Extended Kalman Filter (EKF) algorithm is implemented using two heuristics that allow the release of the filter so that it converges to the correct robot pose after it has started to diverge. Consequently, the results obtained from the different location tests performed are presented and compared, to present the precision achieved and proving the several advantages of using heuristics. Overall, this work with Pozyx system showed that it is a proper and effective tool to improve the robot location in a challenging indoor environment given its good cost/accuracy trade-off

Architecture for multi-technology real-time location systems

[Abstract] Indoor localization is a problem that has generated much interest in recent years. Proximity marketing, eHealth, smart-parking and smart-cities, security and emergency units, logistics management, or industrial control systems are some pf the sectors that have demanded new Location Based Services (LBSs). These services are usually implemented using Wireless Sensor Networks (WSNs), capable of transmitting and receiving Radio Frequency (RF) signals in order to locate mobile devices attached to vehicles, people, or animals. While systems based on satellite systems such as GPS work correctly in outdoor scenarios, indoor localization is still a challenging field of study. On one hand, signal propagation problems are common, not only due to reflections and scattering due to the building structures, but also because of signal attenuation and fading caused mainly by people in movement. To overcome these issues, most of the approaches use several WSNs with a combination of multiple wireless technologies, such asWiFi, ZigBee or Bluetooth, some of them also available in mobile devices such as smartphones and tablets. On the other hand, data received from multiple devices must be filtered and combined by means of location algorithms and techniques in order to obtain precise and robust Real-Time Location Systems (RTLSs). Therefore, it is common to implement hybrid location systems with support for several technologies at the same time. Nevertheless, the development of such systems entails a huge complexity. Thus, one of most widely accepted alternatives is the implementation of software architectures for localization, which provide several benefits. First, accessing to different kinds of hardware devices entails fewer platform and technology restrictions. Second, some common tasks are easier to perform, such as sensor data gathering and storage. Finally, architectures provide utilities for adding and retrieving localization data, user management, or the possibility of using several mapping and coordinate systems. In this work, we present several solutions for implementing software architectures for localization. First, we propose a mono-technology architecture using only Received Signal Strength (RSS) signal levels for ranging, which evolves into a much more complete multitechnology architecture in a second stage. The proposed approaches implement several functionalities that resolve most of the hybrid RTLS system requirements, such as: • Multi-technology. • Support for several coordinate systems and mapping applications. • Data fusion. • Protection and security for both data and user access. • Standardized API for remote access. • Support for off-line data queries, not only on-line data and in real-time. • Depending on different user roles, it eases their tasks at different access levels: registration of WSNs, building blueprints, anchor and mobile node networks registration, generic sensor support, addition and retrieval of measurements and raw sensor data, multiple query support for filtered position estimations, etc. Moreover, we also contributed with different WSN physical layer implementations and experiments. And, due to collaborations with other research groups at different universities we have contributed with a customized hardware and software solution for localization based on RFID technology, as well as with the design of new antenna models based on linear-arrays of Electromagnetically Coupled Patchs (ECPs), valid for improving the WSN communication performance.[Resumo]O problema da localización no interior de edificios foi adquirindo cada vez máis importancia nos últimos anos debido á enorme demanda de novos servizos baseados en localización (LBSs). que apareeeron en todo tipo de sectores como eHealth. marketing por proximidade. smartparking e smart--cities. seguridade e emerxencias. loxística ou control industrial, entre outros. Estes sistemas habitualmente estan baseados na implementación de redes de sensores sen fíos (WSN) capaces de transmitir ou recibir sinais de radio (RF) para localizar dispositivos móbiles. xeralmente adheridos a vehículos. persoas ou animais. Menlres que en exteriores os sistemas de satélites baseados en tecnoloxías corno GPS funcionan correctamente na maioría de entornos. a localización en interiores non é unha tarefa sinxela de resolver e afnda inelúe múltiples retos. Principalmente aparecen problemas de propagación debido ás reflexións e rebotes dos sinais nas estruturas dos edificios. pero tarDén debido a atenuaci6ns e apantallamentos ocasionados xeralrnente por xente en movemento. Para resolver estes problemac;; é necesario implementar ac;; redes de sensores utilizando unha ou varias tecnoloxías sen fíos (como WiFi. ZigBee ou Bluetooth). a1gunhas delas disponibles en terminais sen fíos como smartphones ou tablets. Pero. por outra parte. tamén é necesario o uso de múltiples algoritmos e técnicas de localización para filtrar e posiblemente combinar os datos destas tecnoloxías. permitindo obter así sistemas de localización en tempo real (RTLS) robustos e coa maior precisión posible. Deste xeito. a aproximación máis usual na actualidade para resolver estos problemas é a implementación de sistemas de localización híbridos que soporten múltiples tecnoloxías simultaneamente. Nembargantes. O desenvolvemento destes sistemas leva implícito unha gran complexidade. Unha das alternativas comunmente aceptada é a implementación dunha arquitectura de software para localización, a cal ofrece varias vantaxes. En primeiro lugar, permite minimizar o número de restricci6ns multi-plataforma e multi-tecnoloxía á hora de acceder a distintos tipos de dispositivos hardware. En segundo lugar. facilítase a realización de tarefas comúns como a recolección e o almacenamento das medicións de sensores. Ademais, proporcinánse mecanismos para inserir e recuperar datos de localización ase como xestión de usuarios ou manipulación de múltiple" sistemas de mapas e coordenadas. Neste traballo presentamos varias solucións á hora de implementar arquitecturas de software para localización. comenzando por unha mono-tecnoloxía baseada unicarnente na recolección de niveis de sinal RSS, que evoluciona posteriormente a unha arquitectura multi-tecnoloxía. As solucións propostas ofrecen diferentes funcionalidades que resolven moitos dos problemas asociados aos sistemas híbridos RTLS, entre as que podemos destacar: • Multi-tecnoloxía. • Soporte de múltiples sistemas de coordenadas e de aplicacións de mapas. • Fusión de datos. • Protección e seguridad, tanto de datos como de acceso de usuarios. • API estandarizado para acceso remoto. • Soporte de consultas de datos off-line, non só on-line e en tempo real. • Facilidade de uso para os diferentes usuarios que utilicen a plataforma mediante chamadas a varios niveis: rexistro de WSNs, planos de edificios, rexistro de redes de áncoras e de nodos móviles, soporte de sensores xenéricos, inserción e consulta de medici6ns e de datos sensoriais en ero. inserción e consulta de posicións estimadas por algoritmos de localización, etc. Tamén contribuimos con múltiples implementacións da capa física de WSNs e experimentos. E grazas á colaboración con outros grupos de investigación de diferentes universidades puidemos, por unha parte, contribuir cunha solución de hardware e software para localización baseada en tecnoloxía RFID e, por outra parte, no deseño de novos modelos de antenas baseados en arrays lineais de ECPs, válidos para mellorar o rendemento das comunicacións en WSNs.[Resumen] El problema de la localización en el interior de edificios ha ido adquiriendo cada vez más importancia en los últimos años debido a la enorme demanda de nuevos servicios basados en localización (LBSs), que han ido apareciendo en la industria en sectores de todo tipo como eHeallb, marketing por proximidad, smart-parking y smart-cities, seguridad y emergeocias, logística o control industrial, entre otros. Estos sistemas habitualmeote se basan en la implementación de redes de sensores inalámbricos (WSN) capaces de transmitir o recibir señales de radio (RF) para localizar dispositivos móviles, generalmente adheridos a vehículos, personas o artimales. Mientras que en exteriores los sistemas satelitales basados en tecnologías como GPS funcionan correctamente en la mayoría de entornos, la localización en inleriores todavía plantea múltiples retos y no es una tarea sencilla de resolver. Principalmente aparecen problemas de propagación debido a los reflejos y rebotes de las sefiales en las estructuras de los edificios, pero también debido a atenuaciones y apantallamientos ocasionados generalmente por gente en movimiento. Para resolver estos problemas es necesario implementar Jas redes de sensores utilizando una o varias tecnologías inalámbricas (como pueden ser WiFí, ZigBee o Bluetooth), algunas de ellas disportibles en terminales inalámbricos como smartphones o tablets. Pero, por otra parte, también es necesario el uso de múltiples algoritmos y técnicas de localización, para filtrar y posiblemente combinar los datos de estas tecnologías, permitiendo obtener así sistemas de localización en tiempo real (RTLS) robustos y con la mayor precisión posible. De este modo, la aproximación más usual en la actualidad para resolver estos problemas es la implementación de sistemas de localización híbridos que soporten múltiples tecnologías simultáneamente. No obstante, el desarrollo de estos sistemas lleva implícito una gran complejidad. Una de las alternativas comúnmente aceptada es la implementación de una arquitectura de software para localización, que ofrece varias ventajas. En primer lugar, permite minimizar el número de restricciones multi-plataforma y multi-tecnología a la hora de acceder a distintos tipos de dispositivos hardware. En segundo lugar, se facilitan tareas comunes como la recolección y almacenamiento de las mediciones de los sensores. Además. se proveen mecanismos para insertar y recuperar datos de localización así como gestión de usuarios o manejo de múltiples sistemas de mapas y coordenadas. En este trabajo presentamos varias soluciones a la hora de implementar arquitecturas de software para localización, empezando por una mono-tecnología basada únicamente en la recoleccion de niveles de señal RSS, que se evoluciona posteriormente a una arquitectura mllltitecnología. Las soluciones propuestas ofrecen diferentes funcionalidades que resuelven muchos de los problemas asociados a los sistemas híbridos RTLS, entre las que podemos destacar: Multi-tecnología. Soporte de múltiples sistemas de coordenadas y de aplicaciones de mapas. • Fusión de datos. • Protección y seguridad, tanto de datos como de acceso de usuarios. • API estandarizado para acceso remoto. • Soporte de consultas de datos off-line, no solo on-line y en tiempo real. • Facilidad de uso para los diferentes usuarios que utilicen la plataforma, mediante llamadas a varios rtiveles: registro de WSNs, planos de edificios, registro de redes de anchors y de nodos móviles, soporte de sensores genéricos, inserción y consulta de mediciones y de datos sensoriales en crudo, inserción y consulta de posiciones estimadas por algoritmos de localización, etc. También contribuimos con múltiples implementaciones de la capa física de WSNs y experimentos. y gracias a la colaboración Con otros grupos de investigación de diferentes universidades hemos podido, por una parte, contribuir con una soluciÓn de hardware y software para localización basada en tecnología RFID y, por otra parte, en el diseño de nuevos modelos de antenas basados en arrays lineales de ECPs, válidos para mejorar el rendimiento de las comunicaciones en WSNs