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Indoor Navigation and Personal Tracking System Using Bluetooth Low Energy Beacons

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Indoor Navigation and Personal Tracking System Using Bluetooth Low Energy Beacons UPTEC IT 17 023 Examensarbete 30 hp Oktober 2017 Indoor Navigation And Personal Tracking System Using Bluetooth Low Energy Beacons Adam Hernod Olevall Mathieu Fuchs Abstract Indoor Navigation And Personal Tracking System Using Bluetooth Low Energy Beacons Adam Hernod Olevall, Mathieu Fuchs Teknisk- naturvetenskaplig fakultet UTH-enheten Navigation systems for outdoor purposes have been around us for several years. Recently, it has emerged for indoor use. Thanks to techniques as Bluetooth and Wi- Besöksadress: Fi, public places like shopping malls have come up with solutions that help their Ångströmlaboratoriet Lägerhyddsvägen 1 visitors navigate through the area. At the same time, an approach called crowd- Hus 4, Plan 0 sourced localization has hit the market. It is a technique where people help each other to track down lost assets by getting close enough to an attached device and Postadress: register its coordinates. Box 536 751 21 Uppsala In this thesis, these two techniques were combined to build and evaluate an Telefon: application which handles the indoor positioning system with a new approach. The 018 – 471 30 03 expensive part of the system is already included in the users and visitors Telefax: smartphones. The program consists of positioning the user, while actively 018 – 471 30 00 searching the near area for lost objects. This means that the only cost for the client is to pay for the relatively cheap beacons attached to the walls and ceiling, which Hemsida: are used to locate the users in the building. http://www.teknat.uu.se/student Tests were done to evaluate how accurate the algorithm was when deciding the position of the lost object. The practical number of smartphones required to find the lost asset were also evaluated. Beside these technical aspects a user centered survey was done to understand if people are willing to use such a system. The results showed inconsistency in term of accuracy, but it also showed that it was sufficient for finding the lost asset by this approach. By looking at the results from the different tests that were made a conclusion can be drawn that it requires approximately one user per every 360 to 450 m² to locate an asset below 10 seconds, all depending on the mobility of the asset. Handledare: Per Fork Ämnesgranskare: Edith Ngai Examinator: Lars-Åke Nordén ISSN: 1401-5749, UPTEC IT 17 023 Tryckt av: Reprocentralen ITC Populärvetenskaplig sammanfattning Navigationssystem för utomhusbruk har varit med oss under en lång tid. Nyligen har det även slagit igenom för inomhusbruk och är mer känt under namnet IPS, vilket är en förkortning för det engelska indoor positioning sy- stem. Det mest vanliga sättet som IPS förekommer på idag är att användaren kan se sin position i ett rum genom att dess smartphone tar emot information från sändare som monterats i taket. Dessa sändare är mer kända som beacons och är enbart till för att skicka ut information med hjälp av främst Bluetooth. Förutom IPS har det även på senare tid slagit igenom en helt ny form av lokaliseringsteknik som har den engelska termen crowd locating. Det är ett koncept som bygger på att människor hjälper varandra att lokalisera förlora- de föremål. Det fungerar genom att ägaren till ett föremål fäster en sändare på det. Sändaren kan sedan kommunicera med en applikation som är in- stallerad på en smartphone. Om föremålet sedan förloras kan det lokaliseras genom att någon med applikationen installerad på sin smartphone passerar föremålet och fångar upp sändarens signal. Denna teknik är väl beprövad utomhus, men hittills inte applicerad inomhus. För att lokalisera saker inomhus idag, måste det sitta smarta enheter mon- terade i taken som kan ta emot signalen från den sändare som har fästs vid det borttappade föremålet. Dessa enheter beräknar sedan avståndet till det borttappade föremålet och genom att de kommunicerar med varandra kan de positionera föremålet. Eftersom varje smart enhet måste vara kapabel att kunna skicka och ta emot information samt beräkna data blir kostnaden för varje enhet relativt dyr. Denna rapport syftar till att undersöka möjligheten att kombinera tekniken att navigera inomhus med crowd locating, vilket skulle innebära ett billiga- re system än det ovan nämnda. Genom att kombinera dessa båda tekniker räcker det att installera relativt billiga beacons i byggnaden istället för dyra smarta enheter. Signalerna från dessa beacons tas istället upp av besökarnas smartphones och på så sätt kan det borttappade föremålet lokaliseras. Vida- re i rapporten visas att det gjorts en utvärdering av detta system för att se hur många besökare i till exempel ett shopping-center som måste ha appli- kationen igång för att man snabbt och smidigt ska få ett resultat med bra noggrannhet. Resultatet visar att detta är ett tillvägagångssätt som fungerar. Noggrann- heten i positioneringen av det borttappade föremålet är inte exakt, men det anses bra nog för att vara användbart. Genom att titta närmare på resultaten blir slutsatsen att det i snitt behövs en användare av applikationen per 360 - 450 m2, beroende på om föremålet som eftersöks är rörligt eller statiskt. i Declaration of Authorship We, Adam HERNOD OLEVALL and Mathieu FUCHS, declare that this thesis titled, “Indoor Navigation And Personal Tracking System Using Bluetooth Low Energy Beacons” and the work presented in it is our own. We confirm that: • Where we have consulted the published work of others, this is always clearly attributed. • Where we have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely our own work. • We have acknowledged all main sources of help. • Where the thesis is based on work done by ourselves jointly with oth- ers, we have made clear exactly what was done by others and what we have contributed ourselves. Signed: Date: ii Acknowledgements This thesis was supported by Valtech, Stockholm. We would like to begin by thanking Valtech for the help and support to or- ganize a thesis for us. They really cared for us and arranged a lot of meetings with various people to help us find a good thesis. A big thank you to our supervisor Per Fork (Valtech) for all the support dur- ing this thesis. He was the one with the idea and without him this thesis could not have been done. Thank you, Edith Ngai (Uppsala University) for reviewing this thesis. We are very thankful for her valuable inputs and ideas during this thesis. Also, we want to thank her for taking her time to meet us and discuss the content of the report, this has been really valuable for us. iii “True navigation begins in the human heart. It’s the most important map of all. ” Elizabeth Kapu’uwailani Lindsey iv Contents Declaration of Authorshipi Acknowledgements ii 1 Introduction1 1.1 Background.............................2 1.2 Purpose................................4 1.2.1 Problem Statement.....................5 1.2.2 Goals.............................5 1.3 Delimitations............................5 2 Related Work6 2.1 Indoor Positioning Systems....................7 2.1.1 Indoor Positioning Systems with Bluetooth.......7 2.1.2 Other approaches to IPS..................8 Wi-Fi based localization..................8 Geomagnetic Technology.................8 Image based localization..................8 2.2 Tracking Systems..........................8 2.2.1 Trackr............................9 2.2.2 Personal Tracking via Bluetooth.............9 2.3 Crowd-sourced localization.................... 10 3 Theory 11 3.1 Wireless technologies for IPS................... 12 3.1.1 Bluetooth.......................... 12 3.1.2 Bluetooth Lower Energy.................. 13 3.1.3 Wi-Fi............................. 14 3.2 iBeacon................................ 14 3.2.1 StepInside.......................... 15 3.2.2 Estimote Beacons...................... 16 3.3 Positioning Methods........................ 16 3.3.1 Distance calculation.................... 16 3.3.2 Triangulation........................ 17 3.3.3 Trilateration......................... 18 3.3.4 Proximity.......................... 19 3.3.5 Fingerprinting........................ 20 3.4 Positioning optimization...................... 20 3.4.1 Multilateration....................... 20 v 3.4.2 Least squares........................ 21 3.5 Mobile Operating Systems..................... 22 3.5.1 Android........................... 22 3.5.2 iOS.............................. 22 4 Use Case Scenarios 23 4.1 Child care in shopping malls................... 24 4.2 Asset localization at events, fairs and showrooms....... 24 4.3 Wheelchair tracking at airports.................. 24 5 Method 26 5.1 Comparison between BLE and Wi-Fi............... 27 5.2 Comparison between positioning methods........... 27 5.3 Hardware and software...................... 28 5.4 Accuracy Test: Estimote Beacon.................. 28 5.5 Accuracy Test: Application.................... 28 5.6 Simulation of application usage.................. 29 5.6.1 Scenario 1 - Finding lost child in a mall......... 30 5.6.2 Scenario 2 - Finding a lost table at a trade fair...... 30 5.7 Battery Performance of the Application............. 31 5.8 User Centered Survey....................... 31 6 Implementation 32 6.1 Illustration of the IPS environment................ 33 6.2 Operative system.......................... 33 6.3 Android and BLE communication................ 33 6.3.1 Estimote SDK........................ 34 6.3.2 StepInside SDK....................... 34 6.4 Google Firebase........................... 34 6.4.1 Firebase Realtime Database................ 35
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