The use of smartphones for positioning and navigation is mostly limited to outdoor settings. Indoors, where GPS signals are too inaccurate for positioning, an alternative must be used. This project aimed at producing an indoor positioning system which could be both configured and used by an end user organization without equipping its buildings with proprietary hardware. The prerequisites were that a complete digital representation of the building floors is available, and that the floors have a sufficient amount of WiFi access points. Our system measures radio signal strength from existing WiFi infrastructure using a smartphone. This data is sent to a backend and is used to position a device using two different methods: trilateration and fingerprinting. The finished system can position a user with an accuracy of approximately four meters using fingerprinting instead of trilateration as it yielded the best results. The building used for testing was scanned using a smartphone equipped with our application, something that we would expect an end user to be able to do. / Användandet av smartphones för positionering och navigering är mestadels inriktat på utomhusanvändning. Inomhus är GPS signaler inte tillräckligt noggranna för positionering, och ett alternativ måste användas. Det här projektets mål var att producera ett inomhuspositioneringssystem som kan konfigureras och användas av slutanvändarorganisationen utan att behöva utrusta sina byggnader med proprietär hårdvara. Förutsättningarna är att en komplett digital representation av byggnaden finns tillgänglig, och att våningsplanen har tillräckligt många WiFi basstationer. Vårt system mäter radiosignalstyrka från den existerande WiFi infrastrukturen. Denna data skickas till en backend och används i två olika metoder: trilateration och fingerprinting. Det slutgiltiga systemet kan positionera en användare med en träffsäkerhet på ungefär fyra meter när fingerprintingmetoden används då den producerade det bästa resultatet. Byggnaden som systemet testades i skannades av en smartphone med vår applikation, en sak som vi förväntar oss att en slutanvändare skulle kunna göra själv.

While GPS long has been an industry standard for localization of an entity or person anywhere in the world, it loses much of its accuracy and value when used indoors. To enable services such as indoor navigation, other methods must be used. A new standard of the Wi-Fi protocol, IEEE 802.11mc (Wi-Fi RTT), enables distance estimation between the transmitter and the receiver based on the Round-Trip Time (RTT) delay of the signal. Using these distance estimations and the known locations of the transmitting Access Points (APs), an estimation of the receiver’s location can be determined. In this thesis, a smartphone Wi-Fi RTT based Indoor Positioning System (IPS) is presented using an Unscented Kalman Filter (UKF). The UKF using only RTT based distance estimations as input, is established as a baseline implementation. Two extensions are then presented to improve the positioning performance; 1) a dead reckoning algorithm using smartphone sensors part of the Inertial Measurement Unit (IMU) as an additional input to the UKF, and 2) a method to detect and adjust distance measurements that have been made in Non-Line-of-Sight (NLoS) conditions. The implemented IPS is evaluated in an office environment in both favorable situations (plenty of Line-of-Sight conditions) and sub-optimal situations (dominant NLoS conditions). Using both extensions, meter level accuracy is achieved in both cases as well as a 90th percentile error of less than 2 meters.

No description available.

Positioning of mobile phones or other handheld devices in indoor environments is hard because it’s often not possible to retrieve a GPS-signal. Therefore, other techniques need to be used for this. Despite the difficulties with indoor positioning, the Swedish mining company LKAB want to do exactly this in their processing plants. LKAB has developed an Apple iPhone mobile application to maintain real-time process data and documents for their machines. To retrieve the information an OCR code need to be manually scanned with the application. Instead of manually scanning these codes, LKAB want to develop an Indoor Positioning System that can automatically locate handheld devices in their production plants. This thesis aimed to create a proof of concept Apple iOS application that can position devices without GPS-signals. In the system developed Bluetooth Low Energy iBeacons is used to transmit data to the application. From this data Received Signal Strength Indication values is collected and sent off to a server that transform the values into positioning fingerprints. These fingerprints are used together with the classification algorithms K-Nearest Neighbour to determine in which, on pre-hand created, group the user is located. In these created groups there is a defined set of machines that is being presented back to the user. Test results conducted with the proof of concept application shows that the implemented system works and gives a positioning accuracy of up to 75%.

Whilst GPS positioning has been a well used technology for many years in outdoor environments,a ubiquitous solution for indoor positioning is yet to be found, as GPS positioning is unreliableindoors. This thesis focuses on the combination of Inertial Sensor Dead Reckoning and positionsobtained from the Bluetooth Low Energy (BLE) Direction Finding technique. The main objectiveis to reduce the error rate and size of a BLE Direction Finding system. The positioned object is aMicro-Electrical Mechanical System (MEMS) with an accelerometer and a gyroscope, placed on atrolley. The accelerometer and gyroscope are used to obtain an orientation, velocity vector, andin turn a position which is combined with the BLE Direction Finding position. To further reducethe error rate of the system, a Stationary Detection functionality is implemented. Because of thetrolley movement pattern causing noise in the sensor signals, and the limited sensor setup, it is notpossible to increase the accuracy of the system using the proposed method. However, the StationaryDetection is able to correctly determine a stationary state and thus decreasing error rate and powerconsumption. / GPS är en väl använd teknologi sedan många år, men på grund av dess bristande precision vid inomhuspositionering, behöver en ny teknologi för detta område hittas. Denna studie är fokuserad på Dead Reckoning som ett stöd till ett Bluetooth Direction Finding positioneringssystem. Det främsta målet är att minska felfrekvensen och felstorleken i BLE Direction Finding systemet. Föremålet som positioneras är en Micro-Electrical Mechanical System (MEMS) med en accelerometer och ett gyroskop, placerad på en vagn. Accelerometern och gyroskopet används för att erhålla en orientering, hastighetsvektor och därefter en position som kombineras med den position som ges av BLE Direction Finding. För att minska felfrekvensen ytterligare hos systemet, implementeras en funktionalitet som detekterar om MEMS-enheten är stillastående, kallad Stationary Detection. På grund av vagnens rörelsemönster, som bidrar till brus hos sensorsignalerna, samt den begränsade sensorkonfigurationen, är det inte möjligt att förbättra systemets precision med den föreslagna metoden. Dock kan Stationary Detection korrekt fastställa ett stationärt tillstånd och därmed minska felfrekvensen och energiförbrukningen för enheten.

The work deals with the issue of indoor positiong system in the enviroment of the Android operating system, based on the efficient standard Bluetooth Low Energy, with conjunction of bluetooth beacons. The outcome of master‘s thesis is system topology, description of its components, standards needed for his creation and finally construction of the system.

The indoor positioning system is a series of networking systems used to monitor/locate objects at indoor area as opposed that of GPS which does the same at outdoor. The increase in the popularity of the Internet of Things made the demand for Bluetooth Low Energy technology more and more essential due to their compatibility in the smartphones which makes it to access easier. The BLE’s reliable signal and accuracy in calculating the distance has a cutting edge on others in IPS. In this thesis, the Bluetooth Low Energy indoor positioning system was designed and implemented in the office area, and the positionofIoTdevicesweremonitored. OntheIoTdevices,thebeaconswereplaced. And thesebeaconswerecoveringtheofficearea. Thereceiver,smartphoneinourcase,recorded theReceivedSignalStrengthIndicationofthetransmittedsignalsfromthebeaconswithin the range of the signal and stored the collected data in a database. Two experiments have beenconducted. Oneisforbeaconsthatarestationaryandonethatismoving. Toevaluate these experiments, a few tests were performed to predict the position of beacons based on therecordedreceivedsignalstrength’s. Inthecaseofstationarybeacons, itoffersaccuracy range from 1 m to 5 m, and 3 m to 9.5 m in anticipating the position of each beacon in the case of moving beacon. This methodology was a mixture of fingerprinting and an algorithm of multilateration. Finally, the experiments show that the algorithm used provides the most accurate indoor position using BLE beacons that can be monitored through an Android-based application in real-time.

Context: Indoor positioning systems have become more widespread over the past decade, mainly due to devices such as Bluetooth Low Energy beacons which are low at cost and work effectively. The context of this thesis is to localize and help people navigate to the office equipment, meeting rooms, etc., in an office environment using machine learning algorithms. This can help the employees to work more effectively and conveniently saving time. Objective: To perform a literature review of various machine learning models in indoor positioning that are suitable for an office environment. Also, to experiment with those selected models and compare the results based on their performance. Android smartphone and BLE beacons have been used to collect RSSI values along with their respective location coordinates for the dataset. Besides, the accuracy of positioning is determined by using state-of-the-art machine learning algorithms to train the dataset. Using performance metrics such as Euclidean distance error, CDF curve of Euclidean distance error, RMSE and MAE to compare results and select the best model for this research. Methods: A Fingerprinting method for indoor positioning is studied and applied for the collection of the RSSI values and (x, y) location coordinates from the fixed beacons. A literature review is performed on various machine learning models appropriate for indoor positioning. The chosen models were experimented and compared based on their performances using performance metrics such as CDF curve, MAE, RSME and Euclidean distance error. Results: The literature study shows that Long Short Term Memory and Multi-layer perceptron, Gradient boosting, XG boosting and Ada boosting is suitable for models for indoor positioning. The experimentation and comparison of these models show that the overall performance of Long short-term memory network was better than multiplayer Perceptron, Gradient boosting, XG boosting and Adaboosting. Conclusions: After analysing the acquired results and taking into account the real-world scenarios to which this thesis is intended, it can be stated that the LSTM network provides the most accurate location estimation using beacons. This system can be monitored in real-time for maintenance and personnel tracking in an office environment.

This project has been carried out in, and under the supervision of the Mobile Services Laboratory at the department of Communication Systems, KTH. The task was to explore the technical specifics of the iBeacon technology and its practical limitations in terms of reliability and device placement. In plain text; how close the beacons can be placed to allow for reliable isolation of the pertinent beacon. The main method of reaching the set goal was data capture at certain key positions around the mounted beacons. The resulting data was processed, analyzed and visualized to provide an accessible overview. The measurements and data analysis resulted in fairly concrete data that most of all highlights the very real limitations, and also a word of caution regarding stretching the actual limitations of such a basic technology as it actually is. / Projektet har utförts på, och under överseende av Mobile Services Laboratory på avdelningen Kommunikationssystem vid KTH. Syftet med projektet var att utforska och undersöka de tekniska detaljerna kring iBeacon-teknologin samt dess praktiska begränsningar gällande dess tillförliglighet och fysiska placering av enheterna. I klartext; hur nära iBeacon-enheterna kan placeras samtidigt som en accepterbar nivå av tillförlitlighet gällande separation av den relevanta enheten kan nås. Huvudmetoden som användes för att nå målet var datainsamling på nyckelpositioner runt de monterade enheterna. Framtagen data behandlades, analyserades och visualiserades för att kunna framställa överblickbar data. Mätningarna och dataanlysen resulterade i konkret data som framförallt lyfter de de verkliga begränsningarna för teknologin, samt ett varningens ord om vilka problem som potentiellt kan uppstå när en såpass grundläggande teknik används på ett sätt den eventuellt inte är avsedd för.

This thesis elaborates on the design process of a mobile phone based application for indoor positioning at workplaces. The aim of the application is to ease workflow management and help increase the work efficiency of individuals and teams by reducing the amount of time spent in looking and waiting for each other. In doing so, the research takes a closer look on the user’s perspective on sharing one’s location data. An attempt is made to explore users’ behavior, investigating if any privacy concerns arise out of sharing one’s indoor location data and how it effects the adoption of the service within the context of a workspace. This exploratory approach employed a number of qualitative tools in order to gather data and analyze it. In order to understand the complex context of a work environment where activities (or actions) are defined by a number of factors, actors, mediators, communication channels, etc., the research followed an activity centred approach. The resulting solution is in the form of a service that provides layers of contextual information, responding to the overall activity being performed and the smaller actions that constitute it. A prototype of this application is then taken for user testing. The test results show that the users were hesitant in sharing their location data; citing a number of speculated scenarios where this information may be used in ways that induced a sense of being spied upon. However, in the overall acceptance and adoption of the system, the context of use (the workspace) was found to play a very crucial role.