indoor position detection using wifi and trilateration

indoor position detection using wifi and trilateration
INDOOR POSITION DETECTION USING WIFI AND
TRILATERATION TECHNIQUE
Nor Aida Mahiddin, Noaizan Safie, Elissa Nadia, Suhailan Safei, Engku Fadzli.
Faculty of Informatics, University Sultan ZainalAbidin, Gong Badak
Campus, Terengganu, Malaysia
{aidamahiddin, aizan, elissa, suhailan, fadzlihasan}@unisza.edu.my
ABSTRACT
Various techniques that employ GPS signals
such as A-GPS and GPS transmitters [4, 7]
have been introduced with the hope to
provide a solution for indoor positioning
detection. We proposed the implementation
of trilateration technique to determine the
position of users in indoor areas based on
Wi-Fi signal strengths from access points
(AP) within the indoor vicinity. In this
paper, percentage of signal strengths
obtained from Wi-Fi analyzer in a
smartphone were converted into distance
between users and each AP. A user’s indoor
position could then be determined using a
formula proposed based on trilateration
technique.
KEYWORDS
Indoor Position detection, WI-Fi, Trilateration
Technique.
1 INTRODUCTION
Global Positioning System (GPS) is a
technology developed by United States of
Defense (DoD) that has been used for
military purposed. It is also the main
technology that plays an important role in
satellite navigation. The main purpose of
GPS is to determine the position or
coordinate of an object based on location,
time and speed [2, 6] which provide
Location Based Services (LBS) [5, 6].
Nowadays, the technology has been used
widely in outdoor environment such as in
navigation and coordinate measurements.
GPS depends on satellites to communicate
using radio signals. Common example of
GPS receivers such as GARMIN, NAVMAN
and TOM TOM are capable to determine the
accuracy of a position in the range of 10
meter. Optimum signal performance can be
achieved outdoor but not in indoor
environment. Multipath interference is a
problem that exists in indoor environment
which happens when transmitted signal from
satellite is reflected due to barriers such as
buildings or trees. Weak signal also affect
the accuracy of the position [2, 3]. Wi-Fi
Positioning concepts [4] are among the most
famous solution. Weyn, Maarten, Schrooyen
and Frederik used the combination of
assisted global positioning system (A-GPS)
and Wi-Fi positioning technique using
Wireless Local Area Network (WLAN) to
achieve the accurate coordinates in indoor
environment [4], However A-GPS also have
limitations in indoor environment [6]
because of A-GPS is unable to decode data
from satellites [3]. This paper proposes
indoor position detection using Wi-Fi signal
strength with trilateration technique.
2 RELATED WORKS
Indoor GPS positioning system is a modular
system used to track and locate persons or
objects inside buildings. Nowadays, Indoor
GPS plays an important role in various
domain including consumer’s applications,
emergency services, machines or gadgets
and for military purposes [1]. Referring to
the Federal Communication Committee
362
(FCC) and due to the requirements of
Enhanced 911 (E911) system, GPS is now
embedded into mobile phones which provide
Location Based Services (LBS) [3, 4]. It is
known as assisted GPS or A-GPS which is
built to overcome the limitation of GPS. GPS
is only good for outdoor environment
activities and work poorly in indoor
environment [2]. One of the limitations of
indoor GPS is weak signal acquisition
because GPS signal is weak inside buildings
and cannot penetrate building wall structures
[2, 3] which affect performance in coordinate
measurement or position detection [2].
Network
(RBFNN)
and
Localized
Generalization Error (L-GEM). In a variety
of method, [10] Pseudolite system is used as
an alternative to find the solution of position
location in indoor.
3 PROPOSED METHOD
On the other hand, finger printing is another
alternative in position determination. It
requires comparison of signals from current
measurements with a pre-measured data in
particular locations [5]. There are two phases
in fingerprinting which is offline training
phase and online estimation phase. Wi-Fi
signal strength is an example of offline
training phase in finger printing method.
This paper proposes an indoor position
detection using Wi-Fi signal strength and a
formula to determine position of a user.
Based on the concept of GPS, minimum of
three access points (AP) are needed to
determine the position of a user in an indoor
location. The Wi-Fi signals are in the form
of radio wave where the movements of the
signals are highly dependent on the
frequency. Signals with different diameters
are transmitted by APs in all direction
according to the respective signal strength.
Since wireless routers provide coverage of
about 100 feet (30.5 meters), signal strength
is used to find the collision point in order to
specify the accurate position of an object.
Another solution for position determination
involves detection of proximity such as
Radio-Frequency Identification (RFID) and
Bluetooth technology. Most of the researcher
tries to apply mobile devices in their study.
In Mobile Adhoc Network (MANET),
devices can randomly move in any
directions. Stationary nodes broadcast the
hello massages signals and the node received
the signal automatically determines the
location position itself based on three signals
received from 3 anchor nodes and run the
Kernel AODV platform [9]. Woo et al., [11]
have done experiments at a shield tunnel
construction site using the fingerprint
method of Received Signal Strength
Indication (RSSI) from each Access Point
(AP). Another way to determine position of
object is by using RFID. Based on RFID
concept, Daniel [8] have proposed new
method using a Radial Basis Function Neural
The standard protocol of Wi-Fi is 802.11
which was introduced by Institute of
Electrical and Electronic Engineering (IEEE)
and it is used in wireless LAN [7]. The
standards come in several flavors which are
802.11a that transmits at 5 GHz and can
move up to 54 megabits of data per second.
On the other hand, 802.11b is the slowest
and slightly less expensive and transmits in
the 2.4 GHz frequency and can carry 11
megabits of data per second. Networking
standard 802.11g also transmits at 2.4 GHz
like 802.11b but it is much faster and
theoretically can handle up to 54 megabits of
data per second. 802.11n is the newest
standard to improve speed and range. These
kinds of protocol standards allow
communication via internet through channels
of communication medium that is available
in Wi-Fi. There are 14 channels available in
Wi-Fi where the use of each channel can be
363
selected to avoid interferences in the wireless
transmissions.
This study deploys Wi-Fi technique in
conjunction with IEEE 802.11g networking
standard. Here, we assume the three APs are
known as AP1, AP2, and AP3.
Assume that the coordinates of the three APs
as Figure 1:
Figure 1: Illustration WiFi signal strenght from three
access points
Then, based on three coordinates of the APs,
we need to find the coordinates of the user’s
position that is represented as Z.
Distance, di = p ( 1 – mi )
(1)
Where;
m = is the percentage of signal strength
p = is the maximum coverage of signal
strength
i = 1,2,3
From Figure 1, let each AP be placed at the
center. Assume a scenario where a student
who uses a smart phone, is looking for a
book in a library. Then, we assuming that
signal strength for each AP will spread the
signal in wave forms. The signal strength
will form 3 circles and intersect each other.
The intersection of 3 circles is the position of
user and we want to determine the location
of user who is labeled by B (x, y). To
simplify the calculations, the equations are
formulated so intersection of circle is
occurred at Cartesian plane (see Figure 2).
The equation for any of these circle is as
follow (assuming z = 0):
(2)
The intersection of 3 circles is obtained by
solving systems of linear equations for 2
variables simultaneously. The linear systems
are solved in order to determine the
coordinates x and y.
Let’s assume that a user is using a smart
phones that serves as a receiver of the signals
transmitted from the access points.
Application of Wi-Fi analyzer in the smart
phone presents the signal strength in terms of
percentage. The highest percentage of signal
strength indicates that Z is closest to the AP
whereas the lowest percentage implies that Z
is maximum range of AP.
The percentage of signal strength obtained
from the Wi-Fi analyzer can be converted to
distance between a user’s to each AP using
this equation (Equation 1):
Figure 2: Intersections of 3 circles
364
4 EXPECTED RESULT
Based on Figure 2, we start with the
equations for three circles:
(3)
(4)
From the Figure 1, assuming that all 3
centers are in the fixed Cartesian plane and z
= 0, the 3 coordinates respectively as follow,
AP1 is at (4,4), AP2 is at (26, 10) and AP3 at
(16,26) and B is the user unknown position
at (x,y).
(5)
To determine the location of B, we have to
solve for (x, y, z).
The method to do it is by using systems of
linear equations for 2 variables and solve
these eaquation of linear system à x = b. By
using this method, the jth constraints is used
as a linearizing tool. Adding and subtracting
xj, yj and zj in (3), gives:
(6)
With ( i = 1, 2,... , j+1 , ... , n).
Linear system is easily written in matrix
form Ãx = b,
With
[
]
With the signal strength that received from
the APS, the distance between each APs and
the B can be calculate as follow.
Lets consider, the maximun range of these
APs is 30 meter. Then, from (1), we get the
distance between center of each AP and B as
follow:
d1 = 30 ( 1 – 0.8) = 6m
d2 = 30 ( 1 – 0.7) = 9m
d3 = 30 ( 1 – 0.85) = 4.5m
Then, 3 lines are formed from the 3 circles.
Then, construct the new equation by using
the 3 lines. The equations are as follow:
AP1: (x – 4) 2 + (y – 4) 2 = 62
AP2: (x – 26) 2 + (y – 10) 2 = 92
AP3: (x – 16) 2 + (y – 26) 2 = 4.52
A linear system in 2 variables determines a
collection of planes. The intersection point is
the solution. By solving this equation using
systems of linear equation of 2 variables, we
get a solution for the systems as:
x = 11.97
⃗
[
] ⃗⃗
[
]
(7)
Based on the calculation by using (7), the
position of B is given by (x, y, z).
y = 14.31
Thus, the position of user labeled as B in the
Cartesian plane is (11.97, 14.31). To
determine the exact location in reality, where
is the exact location with coordinates (11.97,
14.31) in the library, we have to perform a
365
simulation showing the exact Cartesian
planes on library. The simulation part is not
cover in this paper.
5.
Rodrigo Vera, Sergio F. Ochoa and Roberto G.
Aldunate (Jan 2011), “EDIPS: an Easy to Deploy
Indoor Positioning System to support loosely
coupled mo-bile work”, pp 365-376.
6.
Paul A Zandbergen (2009), “Accuracy of iPhone
Locations: A Comparison of Assisted GPS, WiFi
and Cellular Positioning”, pp 5-26.
7.
Fluerasu, A., Boiero, G., Ghinamo, G., Lovisolo,
P., & Samama, N. (2010). Indoor Positioning
Using GPS transmitters : Experimental results.
International Conference on Indoor Positioning
and Indoor Navigation (IPIN), (September), 1517.
8.
Daniel, S. (2010). Rfid indoor positioning using
rbfnn with l-gem. Machine Learning, 3(IEEE
Xplore), 11-14. doi:10.1109/ICMLC
6 ACKNOWLEDGEMENT
9.
The authors thank to Mrs. Hasni Hassan and
Mr. Wan Hasbullah from University Sultan
Zainal Abidin. We also thank to Mr.
Zainudin Hat from IP Focus Sdn Bhd for
their valuable help with this research.
Latiff, L. A., Ali, A., Chia-ching, O., & Fisal, N.
(2005). Development of an Indoor GPS-free SelfPositioning System for Mobile Ad Hoe Network
( MANET ). Access (pp. 1062-1067). Ieee.
10. Lee, J. (2010). Indoor initial positioning using
single clock pseudolite system. Information and
Communication Technology, (IEEE), 575-578.
5 CONCLUSION AND FUTURE
WORKS
This paper proposed a method to calculate
the location of a user in an indoor area using
Wi-Fi signal strength with IEEE 802.11g
networking standard based on trilateration
technique. The proposed method serves as a
preliminary step that could be integrated in
future work where we envisage applying the
method by taking into account transmission
barriers such as walls or blocks of large
items.
7 REFERENCES
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Stuart Ingram (July 2006), “UltraWideBand
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George Dedes and Andrew G Dempster
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doi:10.1016/j.autcon.2010.07.009.
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