1. Indoor Localization using Wireless LAN infrastructure Location Based Services Supervised by Prof. Dr. Amal Elnahas Presented by Ahmed Ali Sabbour

2. Location based services Make use of geospatial location information Provide users with emergency, information, tracking and entertainment services.

3. Motivation Previous systems focused on outdoors GPS not suitable for indoor localization Indoor systems used sensor networks Using WLAN infrastructure

4. Aim of the project Design a system to locate users indoors Provide location sensitive information Can be used on campus and in museums

5. Approach System based on fingerprinting approach challenges.. Phase 1 Phase 2 Training Matching

6. Signal instability Challenges

7. Granularity Challenges

8. Number of access points Challenges

9. Number of samples Challenges

10. User orientation Challenges

11. Goals Server able to handle multiple clients Thin client for deployment on small devices Creating a fingerprinting training algorithm Creating a location matching algorithm

12. System design request and response

13. Algorithms Training Fingerprinting algorithm Matching Range based algorithm Euclidean distance based algorithm

14. Fingerprinting algorithm For Each Ref. Point Group by MAC address For Each Group Calculate Frequency Distribution Determine Group Leader Take Reading 30 times

15. Determine Group Leader Start Sort Readings by Frequency Descending Sort Readings by Frequency Descending Get First Reading Get Next Reading current freq = previous? Possible Leaders Leader is mean of possible leaders add to Yes, add to No 50, 47

16. Fingerprinting algorithm For Each Ref. Point Group by MAC address For Each Group Calculate Frequency Distribution Determine Group Leader Calculate Leaders Range Take Reading 30 times (49)‏ (47 to 50)‏

17. Fingerprinting algorithm For Each Ref. Point Group by MAC address For Each Group Calculate Frequency Distribution Determine Group Leader Calculate Leaders Range Calculate Filtered Range Take Reading 30 times (49)‏ (47 to 50)‏

18. Calculate Filtered Range Minimum frequency Freq(Leader) x Threshold = Freq(49) x 50% = 20 x 50% = 10 readings  Readings with frequency < 10 will not be considered. Threshold If we take it as 50%

19. Calculate Filtered Range Filtered Standard Deviation The SD of all readings above the minimum frequency (10). Filtered SD = 1.98 instead of 6.18 Filtered Range The leaders range ± filtered SD. Why filtered standard deviation?

20. Fingerprinting algorithm more groups? For Each Ref. Point Group by MAC address For Each Group Calculate Frequency Distribution Determine Group Leader Calculate Leaders Range Calculate Filtered Range Add Fingerprint to Database No Yes Take Reading 30 times Store room, mac address, filtered range low, filtered range high, leader for the 3 most common access points (49)‏ (47 to 50)‏ (45 to 52)‏

21. Matching algorithm Client side 0030BDF25F32 52 00AF28911235 45 Start Display Location Take Reading Group by MAC address For Each Group Determine Group Leader Construct XML message with Group Leaders Send to Server Parse response XML message 30 times Server side Matching Student class 301 B2 more groups? Yes No

22. Matching algorithm Server side Parse XML message received from client and from the MAC addresses and signal strengths, form an Observed Signal Vector Two algorithms Range based algorithm Euclidean distance based algorithm

23. Matching algorithm Range based For simplicity, assume: Only 2 reference points per room Only 1 access point per reference point A search for a room, given the observed signal vector : MAC: A7C8, Signal: 37 Room 1 is more likely to be the correct location.

24. Matching algorithm Euclidean distance based n represents the number of access points recorded for each reference point, o is the observed signal value and s is a stored signal value in the database. For simplicity of the example, let n = 1, which means that only 1 access point is recorded for each reference point.

25. Matching algorithm Euclidean distance based A search for a room, given the observed signal vector : MAC: A7C8, Signal: 37 Room 1 with reference point 2 is more likely to be the correct location, because it has the minimal Euclidean distance from the observed signal vector.

26. Matching algorithm Server side Server then constructs an XML message with Detected room Coordinates and sends message to the client.

27. Accessing information The client then opens an HTTP connection to the webserver http://webserver/location.php?roomhttp://webserver/location.php?room= The server then constructs an HTML page using PHP with information regarding the location.

28. Results The system is able to detect user's location correctly within 3-4 meters accuracy 70% of the time, using Euclidean based 60% of the time, using range based

31. Thank you.