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Planning an indoor navigation service for a smartphone
 

Planning an indoor navigation service for a smartphone

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Presentation Slides belonging to the MSc Thesis Defense titled

Presentation Slides belonging to the MSc Thesis Defense titled

"Planning an indoor navigation service for a smartphone with Wi-Fi fingerprinting localization"

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    Planning an indoor navigation service for a smartphone Planning an indoor navigation service for a smartphone Presentation Transcript

    • 11:56 Planning an indoor navigation service for a smartphone with Wi-Fi Fingerprinting Localization MSc Thesis Defence Justin StookMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 1
    • 11:56 Contents • Research question • Behind developing an application • Development tools • Wi-Fi Fingerprinting • Localization methods • Tests • Conclusion • Limitations & recommendationsMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 2
    • 11:56 Research question • Is it possible to develop an indoor navigation service for a mobile platform, with the use of only Wi-Fi fingerprinting technology and the framework of the Open Location Services standards, but without using building geometry?MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 3
    • 11:56 From… to… • Using a sensor/method to obtain information • Geocode that information to locations and then: • From locations to graphs • From graphs to navigation • Checking location estimations if correct • Checking routes if correctMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 4
    • 11:56 Setting up the framework: BEHIND THE DEVELOPMENT OF AN APPLICATIONMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 5
    • 11:56 The interfaceMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 6
    • 11:56 Developing an application … • Hardware – HTC Desire Z • Software – Android 2.2.1 – Eclipse SDKMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 7
    • 11:56 … using a framework • Open Location Services from Open Geospatial Consortium – Gateway services – Geocoding services – Directory services – Navigation (routing) services – Presentation servicesMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 8
    • 11:56 OGC Adjustments • Gateway services: – Usage of Wi-Fi • Geocoding services: – Use room numbers • Navigation services: – Routing and routing directionsMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 9
    • 11:56 Why no geometry of buildings? • Advantage: – No difficult map matching algorithms needed – Leads to simple text based output • Disadvantage: – Parameters to set up maps cannot be used – Parameters of OGC Services cannot be used; only a frameworkMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 10
    • 11:56 Using a sensor technology WI-FI FINGERPRINTINGMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 11
    • 11:56 Wi-Fi • Advantages: – Available in most public buildings – Wi-Fi enables applications – Multipath (reflectance, absorption) resistant • Disadvantages: – Multipath instable over time – Multipath dependent on objects presentMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 12
    • 11:56 Fingerprinting • Utilizing signal strengths from routers – The stronger the signal (dBm), the closer • Each spot has a profile of signal strengths – A fingerprintMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 13
    • 11:56MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 14
    • 11:56 FingerprintingMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 15
    • 11:56 From fingerprints to locations • At specific places, signal strengths characteristics differ enough • Couple information to it • Connect those places with each other: – Getting a navigable graph!MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 16
    • 11:56 Simple GraphsMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 17
    • 11:56 Simple GraphsMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 18
    • 11:56 Remarks on fingerprinting • Unlike the outdoor world, not each single space can be recorded – Assumes locations to be distinguished by fingerprints • How to deal with unstable signals? • How to convert changing signals to locations again?MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 19
    • 11:56 How to use fingerprinting for localization? USING FINGERPRINTING FOR LOCALIZATIONMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 20
    • 11:56 AveragingMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 21
    • 11:56 Localization methodology How to use the fingerprints? • Two methods proposed: – Least sum of squares – Counting matching in a search space rangeMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 22
    • 11:56 Localization methodology I • Least sum of squares: – Compares LIVE signal strength with RECORDED signal strength – Sums all squared differences per location – Location with the least sum of squared differences is the actual locationMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 23
    • 11:56 Localization methodology I • At a certain spot at a certain time, you receive the following signal strengths: • A: -62 dBm • B: -64 dBm • C: -64 dBm • D: -73 dBm • What should my location be?MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 24
    • 11:56 Localization methodology I • 1 = 26; 2 =254  Location is 1Location MAC SS Rec SS Live Dif Dif² ∑(Dif²)1 A -61 -62 1 11 B -64 -64 0 01 C -69 -64 -5 25 262 B -71 -64 -7 492 C -77 -64 -13 1692 D -79 -73 -6 36 254MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 25
    • 11:56 Localization methodology I • Each location must have an equal amount of MACs, otherwise Sum based on unequal cases – What about weighting (sum divide by number of cases)? – Counter with ratio: 3 out of 4, 5 out of 10? – Android thinks different…MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 26
    • 11:56 Localization methodology I • Assumes all routers should be in reach – Causing false predictions in locations • If value blank: no value = lower amount of SS – Easily lower values obtained with missing values • If value low (-100 dBm) = low amount if recorded signal was weak already – Compare recorded value of -80 and -60 to -100 if it was out of range… • Biased? • Android does not handle NO DATA wellMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 27
    • 11:56 Localization methodology I • Leaving BLANK – RESULT = BLocation MAC SS Rec SS Live Dif Dif² ∑(Dif²)1 A -61 -69 8 641 B -64 -63 1 11 C -69 - - - 652 B -71 -63 -8 642 C -77 - - -2 D -79 - - - 64MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 28
    • 11:56 Localization methodology I • Inserting -100 – RESULT = ALocation MAC SS Rec SS Live Dif Dif² ∑(Dif²)1 A -61 -69 8 641 B -64 -63 1 11 C -69 -100 31 961 10262 B -71 -63 -8 642 C -77 -100 23 5292 D -79 -100 21 441 1034MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 29
    • 11:56 Localization methodology I • Solution: using no data within the Least Sum of Squares – This has not been assessedMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 30
    • 11:56 Localization methodology II • Counting within a search space: – Using an upper range or lower range to be searched (e.g. ± 3-5 dBm). – If recorded MAC value is -60, a match is done when the live MAC value is within reach of that - 60 (such as [-63,-57], [-64,-56]) – Location with the most matches is the most likely locationMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 31
    • 11:56 Localization methodology IIMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 32
    • 11:56In Theory… Wishful thinking…MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 33
    • 11:56 Localization methodology II • Yet: – Each location must have an equal amount of MACs, otherwise maximum criteria would not be fair – Correcting with a ratio? 5 out of 8 vs 5 out of 10 • Seems to be appropriate methodMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 34
    • 11:56 Does it work? – Combining application with methodology TESTING THE COUNT LOCALIZATION METHODOLOGYMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 35
    • 11:56 Location testing • Testing whether locations were correct • 20 scans for 9 diverse locations with two datasets: – Type I: Using the 10 strongest signals at recording phase – Type II: Using signals originating from AP’s in same spaceMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 36
    • 11:56MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 37
    • 11:56 50 Exact location testing – type I 45 40 14 / 180 (7,8%) 35 3211 30 3209 3203 25 3110 3105 20 3101 3011 15 3007 3002 10 5 0 Exact match Within 1 places Within 2 places Beyond 2 Beyond 2 Within 2 Within 2 places, same floor places, other floor places, other floor places, behind areaMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 38
    • 11:56 46 / 180 (25,6%) 50 Location testing – type II 45 40 35 3211 30 3209 3203 25 3110 3105 3101 20 3011 3007 15 3002 10 5 0 Exact match Within 1 places Within 2 places Beyond 2 Beyond 2 Within 2 places, same floor places, other floor places, other floorMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 39
    • 11:56 Location testing • Improvement of location estimation using specific MACs • Still very weak results!MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 40
    • 11:56 Repeating  route testing I • Five different routes, repeated three times: – Correct match (on route) – Near correct match (not appropriate location, but the location given is on the list) – Incorrect match (off route)MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 41
    • 11:56 Route testing IMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 42
    • 11:56 Route testing IRoute Characteristic Nodes On On/off Off On On/off Off1 South End 0F - North End 2F 27 12 8 7 44.4% 29.6% 25.9%2 South End 1F - North End 1F 33 12 8 13 36.4% 24.2% 39.4%3 South End 2F - North End 1F 36 16 10 10 44.4% 27.8% 27.8%4 South End 0F - South End 2F 21 9 1 11 42.9% 4.8% 52.4%5 North End 2F - North End 0F 21 8 3 10 38.1% 14.3% 47.6%Total 138 57 30 51 41.3% 21.7% 37.0%MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 43
    • 11:56 Route testing II • Less than 50% correct route confirmations has been provided • Navigation is more than providing actual locations, but also route confirmation – do you always need te actual location? – Route confirmation can also made simple such as: “you are on route if you see the office room numbers adding up”MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 44
    • 11:56 Did it work? What are the limitations and recommendations? CONCLUSION AND REFLECTIONMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 45
    • 11:56 Conclusion • No, it is not possible to create an indoor navigation application for a smartphone, based on Wi-Fi fingerprinting, which does not utilize building geometry: – Wi-Fi signals too unstable and fluctuate too much – Quality of fingerprints dependent on receivers – Treatment of Android for SS Method; Count method too sensitive to changes – Insufficient amount of correct matches (<50%)MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 46
    • 11:56 Conclusion – Dynamic MACs requires frequent updates – Text based is not enough; enhancements required (images, graphs, photos, icons) – Orientation aspects difficult to achieve without geometry: neutral directions insufficient • Route confirmation still possible: “you are correctly on your way if you see the room numbers adding up” – OpenLS parameters cannot be used without geometry, only the frameworkMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 47
    • 11:56 Limitations • Localization methods: – Count method: too susceptible to unstable signals – Random matching, sub setting – SS method: Android deals “unfair” (either -100 assignment or leaving out) – Working with ratio’s and countering bias? • Routing methods: – Confirmation less than 50% correct – No geometry = no orientation  hard to find compromises to steer the userMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 48
    • 11:56 Limitations • Standards: – OGC proves to be useless aside from the framework • Platform: – Android does not handle well (same as localization method)MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 49
    • 11:56 Limitations • Modelling: – Not for each location possible – Differences in recordings (laptop vs. phone) – Multipath still an issue • Recording macs a legal issue – Restricted to public buildings • Presentation: – Maps, images and icons preferable way of communicatingMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 50
    • 11:56 Recommendations (approaches) • Enhancements in localization methodology – How about combining SS with Count? – Improvement/treatment of Least Sum of Squares with NO DATA – Working with ratio’s and countering bias • Combining with different sensor techniques (BlueTooth, UltraWideBand) – Robustness in signals more guaranteed? • Allowing on the fly fingerprint recording (automatization processes)MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 51
    • 11:56 Recommendations (approaches) • Uploading/downloading from server databases • Study of legal issues • Maintenance of databases: finding a solution for difference is receivers/signal strengths • Comparison of applications: – EKAHAU: Locating system – why does that work? • Other location algorithm, 2-way, Tag management, Map/zone managementMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 52
    • 11:56 Recommendations (approaches) • Source: Ekahau (2011)MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 53
    • 11:56 Recommendations (application) • Enhancements in: – Routing (via inclusion/exclusion) – User profiles (wheelchair, staircase avoider) – Presentation (images, icons, photos) – Design • Combination with other smartphone elements: – Augmented reality – Accelerometer • Improvements (if successful localization) in: – Converting (geocoding) fingerprints to real world coordinatesMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 54
    • 11:56 Acknowledgement • CBP, College Bescherming Persoonsgegevens (2011), Last onder dwangsom / opdracht tot vernietiging payload data. Retrieved [April 19 2011]: http://www.cbpweb.nl/downloads_pb/pb_20110419_brief_google_lod.pdf • Hsu, C.C. (2011), Factors affecting webpage’s visual interface design and style. Procedia Computer Science 3, pp. 1315-1320. • Shum, K.C.Y, Q.J. Cheng, J.K.Y Ng & D. Ng (2011), A Signal Strength based Location Estimation Algorithm with a Wireless Network. 2011 International Conference on Advanced Information Networking and Applications, pp. 509-516. • Zhang, D. et al. (2010), Localization Technologies for Indoor Human Tracking. Paper submitted to the 5th International Conference on Future Information Technology (FutureTech), May 2010, Busan, Korea. Retrieved [January 26 2011] http://arxiv.org/ftp/arxiv/papers/1003/1003.1833.pdf. • And many thanks to Mehmet Yildirim (TU Berlin) and Geert Wirken (UU) for co- writing and debugging the Java codes.MSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 55
    • 11:56 Questions? THANK YOU FOR YOUR ATTENTIONMSc Thesis Defence Wi-Fi Indoor Localization with a smartphone 56