Indoor Localizationusing Wireless LAN infrastructure

Location Based Services

Supervised byProf. Dr. Amal Elnahas

Presented byAhmed Ali Sabbour

Location based services

Make use of geospatial location information

Provide users withemergency, information, tracking and

entertainment services.

Motivation

Previous systems focused on outdoors

GPS not suitable for indoor localization

Indoor systems used sensor networks

Using WLAN infrastructure

Aim of the project

Design a system to locate users indoors

Provide location sensitive information

Can be used on campus and in

museums

Approach

System based on fingerprinting approach

challenges..

Phase 1

Phase 2

Training

Matching

Signal instabilityChallenges

GranularityChallenges

Number of access pointsChallenges

Number of samples

60 61 62

0

1

2

3

4

5

6

7

8

9

10

10 Readings

RSSI

FR

EQ

21 45 56 58 60 61 62

0

10

20

30

30 Readings

RSSI

FR

EQ

21 22 45 49 51 54 56 58 60 61 62 63 64

0

10

20

30

40

50

50 Readings

RSSI

FR

EQ

17 18 20 21 22 44 45 48 49 50 51 52 53 54 55 56 57 58 60 61 62 63 64

0

10

20

30

40

50

60

70

80

90

100

100 Readings

RSSI

FR

EQ

Challenges

User orientation

20 24 26 27 28 29 30 32 33 38 41 42 43 44 45 46 49 50 51 54 55 57 58

0

10

20

30

North

18 23 26 28 30 33 41 43 45 49 51 54 56 58 63

0

10

20

30

South

2023242627282930323338414243444546495051545557586364

0

10

20

30

East

18 23 26 28 30 33 38 42 44 46 49 51 54 56 58 63

0

10

20

30

West

Challenges

Goals

Server able to handle multiple clients

Thin client for deployment on small devices

Creating a fingerprinting training algorithm

Creating a location matching algorithm

System design

requestand

response

Algorithms

TrainingFingerprinting algorithm

• MatchingRange based algorithmEuclidean distance based algorithm

Fingerprinting algorithmFor EachRef. PointFor EachRef. Point

Group byMAC address

Group byMAC address

For EachGroup

For EachGroup

CalculateFrequencyDistribution

CalculateFrequencyDistribution

DetermineGroup Leader

DetermineGroup Leader

Take ReadingTake Reading30 times

Determine Group LeaderStartStart

Sort Readingsby FrequencyDescending

Sort Readingsby FrequencyDescending

Get FirstReadingGet FirstReading

Get NextReadingGet NextReading

current freq= previous?current freq= previous?

PossibleLeadersPossibleLeaders

Leader is mean ofpossible leaders

Leader is mean ofpossible leaders

add to

Yes, add to

No

50, 47

50472

=48.5≈49

Group A7C8

RSSI Frequency

50 20

47 2045 1136 625 3

Fingerprinting algorithmFor EachRef. PointFor EachRef. Point

Group byMAC address

Group byMAC address

For EachGroup

For EachGroup

CalculateFrequencyDistribution

CalculateFrequencyDistribution

DetermineGroup Leader

DetermineGroup Leader

CalculateLeadersRange

CalculateLeadersRange

Take ReadingTake Reading30 times

(49)

(47 to 50)

Fingerprinting algorithmFor EachRef. PointFor EachRef. Point

Group byMAC address

Group byMAC address

For EachGroup

For EachGroup

CalculateFrequencyDistribution

CalculateFrequencyDistribution

DetermineGroup Leader

DetermineGroup Leader

CalculateLeadersRange

CalculateLeadersRange

CalculateFilteredRange

CalculateFilteredRange

Take ReadingTake Reading30 times

(49)

(47 to 50)

Calculate Filtered Range

Minimum frequencyFreq(Leader) x Threshold = Freq(49) x 50% = 20 x 50% = 10 readings

Readings with frequency < 10 will not be considered.

ThresholdIf we take it as 50%

Group

RSSI Frequency

50 20

47 2045 1136 625 3

Calculate Filtered Range

Filtered Standard DeviationThe SD of all readings above the minimum frequency (10).

Filtered SD = 1.98 instead of 6.18

Group

RSSI Frequency

50 20

47 2045 1136 625 3

Filtered RangeThe leaders range ± filtered SD.

Why filtered standard deviation?

Fingerprinting algorithm

moregroups?more

groups?

For EachRef. PointFor EachRef. Point

Group byMAC address

Group byMAC address

For EachGroup

For EachGroup

CalculateFrequencyDistribution

CalculateFrequencyDistribution

DetermineGroup Leader

DetermineGroup Leader

CalculateLeadersRange

CalculateLeadersRange

CalculateFilteredRange

CalculateFilteredRange

AddFingerprintto Database

AddFingerprintto Database

No

Yes

Take ReadingTake Reading30 times

Store room, mac address, filtered range low, filtered range high, leader

for the 3 mostcommon access points

(49)

(47 to 50)

(45 to 52)

Matching algorithmClient side

<message> <group> <mac>0030BDF25F32</mac> <signal>52</signal> </group>

<group> <mac>00AF28911235</mac> <signal>45</signal> </group></message>

StartStart

DisplayLocationDisplayLocation

Take ReadingTake Reading

Group byMAC address

Group byMAC address

For EachGroup

For EachGroup

DetermineGroup Leader

DetermineGroup Leader

ConstructXML message

withGroup Leaders

ConstructXML message

withGroup Leaders

Sendto Server

Sendto Server

Parse responseXML message

Parse responseXML message

30 times

Server sideMatching

Server sideMatching

<reply valid=”1”><room id=”23” x=”50” y=”23”>

<name>Student class</name><num>301</num><building>B2</building>

</room></reply>

moregroups?more

groups?

Yes

No

Matching algorithmServer 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

Matching algorithmRange based

Room ID Ref Point MAC Low Range High Range Stored1 1 A7C8 35 42 381 2 A7C8 35 41 372 1 A7C8 34 38 352 2 A936 20 22 21

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.

Matching algorithmEuclidean distance based

distance=∑i=1

n

o i−si2

n represents the number of access points recorded for each reference point, o is the observed signal value ands is a stored signal value in the database.

simplified distance=o−s2

For simplicity of the example, let n = 1, which means that only 1 access point is recorded for each reference point.

Matching algorithmEuclidean distance based

Room ID Ref Point MAC Low Range High Range Stored1 1 A7C8 35 42 381 2 A7C8 35 41 372 1 A7C8 34 38 352 2 A936 20 22 21

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 theminimal Euclidean distance from the observed signal vector.

o− s 2

3 7− 3 8 2=1

37−37 2=0

37−35 2=2

Matching algorithmServer side

Server then constructs an XML message with

Detected room Coordinates

and sends message to the client.

Accessing information

The client then opens an HTTP connection to the webserver

http://webserver/location.php?room=<id>The server then constructs an HTML page using PHP with information regarding the location.

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

Thank you.