International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Seminar on Recent Advances in Wireless Networks and Communications, NWNC-2014
A Review of Location Detection Techniques in Wi-Fi
Ashutosh Kuntal
Madan Lal Tetarwal
Purnendu Karmakar
The LNMIIT
Jaipur, Rajasthan, India
The LNMIIT
Jaipur, Rajasthan, India
The LNMIIT
Jaipur, Rajasthan, India
ABSTRACT
Recently, there has been much focus on localization of mobile
node using wireless local area network. Location information
of a mobile node is used to provide different location based
services to the user. Intruder identification and possible threat
can be detected early if the physical location of the node is
known beforehand. There are number of localization
techniques to determine the location of a mobile node in
indoor and outdoor environments. In this paper, we provide an
overview of typical location estimation schemes of triangulation, scene analysis, and proximity particularly for
localization of mobile node in indoor environments by using
wireless local area networks.
Keywords
Global positioning system, WLAN, location finger printing,
RTT, localization
1. INTRODUCTION
In recent years, location sensing system became very popular.
These systems provide a new type of services called
automatic object location detection. Location detection is used
in many real world applications. The estimation of physical
location of a mo-bile node in a wireless network is called as
localization. The main motive of the process is to get the
location of mobile node. There are different techniques to find
the location of the mobile nodes. At present, NAVSTAR
Global Positioning System (GPS) is widely used for
localization of mobile devices [1]. There are many mobile
nodes which are working with GSM, UMTS etc and are
equipped with GPS. But these solutions leads to increase in
cost, battery consumption etc [2], and also not suitable for use
in urban areas. In the last few decades, the location estimation
problems have been investigated extensively but still there are
some open issues that re-main unresolved. The main
challenge in localization is to estimate the location efficiently
and precisely in non line-of-sight (NLOS) scenarios. NLOS
scenarios occur when there is an obstruction between
transmitter (TX) and receiver (RX) which are commonly encountered in modern wireless system deployment for both
indoor and outdoor environments. In such type of
circumstances it became impractical to use global positioning
system (GPS).since WLAN access is now widely available,
there is a high demand for accurate positioning in indoor and
outdoor environments in WLANs [3], [4].
Several wireless technologies are used for wireless indoor
localization which can be classified based on: 1) the location
positioning algorithm; 2) the physical layer or location sensor
infrastructure, i.e., the wireless technology used to
communicate with the mobile nodes or static nodes. An
indoor wireless positioning system consists of a signal
transmitter and a measuring unit. The measuring unit carries
the major part of system intelligence.
Several previous works have been reported in the literature
(e.g., [5]-[9]) that provide extensive review on localization
using different techniques like angle-of-arrival (AOA), time-
of-arrival (TOA), time difference-of-arrival (TDOA), and
received-signal-strength (RSS) techniques. The location of
mobile node is estimated by using different metrics of radio
frequency signal like RSS, angle and time required to travel
from transmitter to receiver.
This review paper is organized as follows. Section II defines
the wireless geolocation system and the different positioning
algorithms with their advantages and disadvantages are
explained in section III. At last, Section IV concludes this
review paper.
2. WIRELESS GEOLOCATION
SYSTEM
The wireless geolocation system is divided in to three parts,
first is location sensing device that determines the relative
location of a mobile node by receiving the radio frequency
signal, second is positioning algorithm that uses the data
provided by location sensing device to estimate the position of
a mobile node and the last is a display system used to display
the computed position of the mo-bile device [10]. Wireless
geolocation systems can be categorized into two parts 1)
Mobile assisted and 2) Network assisted. In mo-bile based
geolocation systems, the mobile node estimates its own
location by using radio frequency signals transmitted from
refer-ence nodes. In network based geolocation systems,
reference node measures the signal transmitted from mobile
node and relay these measurements to central processing site.
After estimating the location of mobile node, central
processing site relay this information to mobile node.
Fig. 1. Wireless Geolocation System [10]
The main advantage of network based geolocation system is
that the mobile nodes are not involved in location estimation
process and hence it saves battery power of mobile node. The
location of a mobile node can be determined by using any one
of localization techniques, Propagation based [11], [12], [15]
and Location fingerprinting [11], [13], [14].
3. POSITIONING ALGORITHMS
Localization of a mobile node in WLAN is typically
performed as illustrated in figure 1. In WLAN a group of
nodes coordinate to initialize the localization. One or more
access point emits a signal, and some property of signal is
29
International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Seminar on Recent Advances in Wireless Networks and Communications, NWNC-2014
observed by one or more mobile node. The position of mobile
node is then determined by position estimation using different
localization algorithm. It is necessary to maintain a list of all
the cooperating nodes which are deployed at known positions
a priori. These devices are referred to as fixed, anchor or
infrastructure nodes. The absolute position can be obtained if
the positions of anchor nodes are known with respect to some
global coordinate system. The position of a mobile node can
be estimated by using triangulation, scene analysis, proximity
or Doppler shift based methods. These algorithms have
unique advantages and disadvantages for specific applications
and services.
3.1 Triangulation
Triangulation is the process of determining the location of a
point by measuring angles to it from known points instead of
measuring the distances to the point directly. In triangulation,
the distance of a mobile node is calculated from several fixed
or anchor nodes and this distance is considered as the radii of
many circles with centers at every mobile node. The anchor
nodes in WLAN are access points. Thus, the location of a
mobile node is the intersection of all the circles as shown in
Fig. 3.
3.1.1. Lateration techniques
Lateration is the process of deter-mining the location of a
point using the geometric properties of circles, spheres or
triangles. In lateration instead of measuring angle, the distance
between mobile node and reference node is measured.
multipath means that the several multipath signals arrive just
after the line of sight signal and their cross-correlation change
the location of the peak from the line of sight signal. As the
length of path decreases the signal strength of line of sight
increases in comparison with the multipath signals [18]. In
TOA based systems, transmitter and receiver should be
precisely synchronized and a timestamp must be labeled with
the transmitting signal. All the receivers after calculating their
TOA forward it to the transmitter. Where the position is
estimated using these TOAs. Different signaling techniques
are used to measure TOA like direct sequence spread
spectrum (DSSS) [19] and UWB [20].
Finally, TOA can be measured using different signaling
techniques, such as direct sequence spread spectrum (DSSS)
[15], [16] or UWB [17], [21]. The distance between mobile
node (MN) and access point (APi) is given by
,
(2)
Where, c is the speed of light as the radio frequency signals
used in WLAN travels with the speed of light and t 0 is the
time when transmitter starts transmission. ti is the TOA of MN
signal to APi in TOA, the distance calculated from three APs
are combined to estimate the position of MN.
The coordinates of MN (X m,Ym) can be calculated using
following set of equations (see figure 2) and assume that AP1
is at origin.
(3)
3.1.1.1 TOA (time of arrival)
TOA is the time taken by the signal for its arrival to the
receiver from transmitter. The measured TOA includes the
transmission plus a propagation induced time delay, Ti;j. The
time delay Ti;j is equal to the transmitter-receiver separation
distance, Di;j ,divided by propagation velocity Vp . The key
element of TOA technique is the receiver’s ability to
accurately estimate the arrival time of the line-of-sight (LOS)
signal. This arrival time estimation is affected by additive
noise and multipath signals. Additive noise is the major
source of error in TOA measurements. The simple crosscorrelator (SCC) is used to estimate the TOA. A generalized
cross-correlator (GCC) is evolved in [16] which enhances the
simple cross-correlator by applying the prefilters. The spectral
components of the signal having little noise are amplified by
the prefilters and the components with large noise are
attenuated. The lower bound on variance of TOA is provided
by Cramer Rao bound (CRB). Signal and noise power are
constant over the whole signal bandwidth for a signal having
the bandwidth B (in Hz) and center frequency Fc (in Hz),
where Fc is much greater than B [9]. According to CRB on
TOA estimates, variance is linearly related to the inverse of
signal to noise ratio (SNR) experienced over the radio link
[17].
(1)
Where, Ts is the signal duration in seconds. The bound
predicted by the CRB in (1) can be achieved in multipath-free
channels by designing the system for high value of signal to
noise ratio. Thus (1) defines how the time duration, bandwidth
and SNR affect the estimation of TOA. For example, if the
bandwidth is doubled then the variance of TOA estimation is
reduced to half. The error in estimation of TOA is greater due
to multipath channels in comparison to additive noise [9].
Early arriving multipath and attenuated line of sight signal are
responsible for error in TOA estimation. Early arriving
(4)
(5)
Now, the coordinates of MN can be calculated by solving
these three equations. It is not possible to include TOA
measurement from more than three APs. This issue can be
solved by combining all the measurements by using least
square solution as follows. Subtracting equation (3) from (4)
gives
Similarly subtracting equation (3) from (5) gives
The above two equations can be written as
(6)
Where,
(7)
We can write equation (6) as
(8)
Where,
,
,
(9)
The solution of equation (6) is given by [19]
30
International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Seminar on Recent Advances in Wireless Networks and Communications, NWNC-2014
(10)
Figure 3: TDOA measurements for location estimation
Equation (12) and (13) can be written as
(14)
Figure 2: Trilateration method for location estimation
Where,
There are several other algorithms like closest-neighbor (CN),
and the residual weighting (RWGH) for TOA based WLAN
location systems [22]. In CN algorithm, the MN location is
estimated as the location of AP or reference point that is
closet to MN. In RWGH algorithms, the location is estimated
by determining the intermediate Linear Square estimate for
different sets of TOA measurements.
3.1.1.2 Time difference of arrival (TDOA)
TDOA is an advance version of TOA to avoid the problems of
packet loss and synchronization [23]. In wireless sensor
networks TDOA is commonly used for lo-cation detection. In
TDOA, the difference between TOAs is used to detect a MN
position. TDOA is the difference of time of ar-rivals from
different APs to MN. There is no need of synchroniza-tion
between APs but it requires synchronization between MNs. In
WLAN, two values of TDOA are required to locate a MN.
The constant time difference of arrival of two APs (AP1, AP2
and AP2, AP3) defines a hyperbola because the difference of
range is also constant. The intersection of two hyperbolic
shapes will be the position of MN (Figure 3). RTT can be
measured between two nodes to estimate the distance between
nodes if they don’t have a common clock [24]. The major
source of error in TDOA measurement is the loose
synchronization and inaccurate estimation of time of arrivals.
TDOA associated with AP2 is the difference of time of
arrivals of radio frequency signal at the mobile node from
AP1 and AP2. The difference of distances between MN to
AP1 and MN to AP2 is given by
Now we can write equation (4) in terms of TDOA as
,
,
,
Now the solution for x can be obtained by
(15)
TDOA measurement is highly affected by multipath signals
and non line of sight (NLOS) transmission of signals. The
error caused by these effects can be reduced by using several
NLOS mitigation techniques described in [25]. A TDOA
localization method, which reduces time synchronization
errors within 5nsec by using highly accurate OCXO (oven
controlled crystal oscillator) is proposed in [26]. TDOA
technique doesn’t need extra hardware for measurement of
TDOA but it requires an accurate and precise synchronization
between reference nodes.
3.1.1.3 RSS
RSS is the received signal strength in voltage measured by the
inbuilt circuit of receiver. Received signal strength indicator
display this signal strength in terms of dBm. In RSS based
technique, the received signal strength of radio frequency
wave is measured by each receiver during the normal data
transmission. At least three reference nodes are required to
determine the 2-D location of a mobile node [27]. This
measured signal strength can be used to calculate the distance
because the received power is inversely proportional to the
square of distance [28].
(16)
, as
(11)
By using equation (3) and (7) in equation (11), we get
And by rearranging these terms, we get
Where, Pr is the received power at a distance d. From above
equation, we can calculate the distance by comparing the
transmitted power and received power. RSS of radio
frequency signal cab be retrieved from every access point.
The radio frequency signal follows the propagation based
theorem or path loss equation. The distance can be calculated
using the path loss equation.
(12)
(17)
Similarly by solving equation (5), we get
(13)
Where, P0 is the received power at minute distance d0 and α is
the attenuation constant or path loss constant, typically
between two and four. This propagation theorem does not take
31
International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Seminar on Recent Advances in Wireless Networks and Communications, NWNC-2014
into account the effect of walls and multipath signals.
Therefore the distance estimated by using this theorem is
overestimated. This effect can be severe in indoor
environments like a building which have many rooms isolated
by walls. To take into account the radio frequency signal
attenuation from walls a signal propagation model is
introduced in [29] as shown in equation (18).
(18)
Where, n is the number of walls and Wa is the attenuation
factor due to walls.
Now the distance can be calculated by using the equation (17)
[28]:
(19)
direct sequence and frequency hop are used. Shadowing is the
attenuation of signal due to the obstructions.
3.1.1.4 RTOF
The RTOF is the time taken by the signal to travel from
transmitter to receiver and back to the transmitter. The
distance calculation in RTOF is same as that of TOA. In
WLAN round trip time (RTT) or RTOF can be measured by
using RTS and CTS frames. RTOF can be measured from the
last segment of RTS frame sent to the first segment of the
CTS frame received. Approximately 300 RTT values are
required to accurately estimate the distance between mobile
node and reference node [32]. To calibrate the time taken by
the reference node to process the query, a set of RTT
estimation with zero distance between mobile node and
reference node is obtained. Now the RTOF can be calculated
by using [32]
(25)
Where, RTTd is the RTT, when mobile node is at distance d
from reference node and RTT0 is measured RTT when
distance is zero.
Now the distance between mobile node and reference node
can be calculated by
(26)
Fig. 4. Trilateration for RSS based localization technique
The distance of mobile node is calculated from at least three
reference nodes and then the location of mobile node is
estimated by using trilateration method. Suppose R1, R2 and
R3 are three reference nodes used to locate a mobile node M
as shown in Fig 4.
The distance d1, d2 and d3 are given by using Pythagorean
Theorem as shown in following equations
, i=1,2,3
(20)
These equations are solved to get coordinates of mobile node
M and given by [30]
(23)
(24)
Where,
And
The distance measurement using RSS is mainly affected by
two things, multipath signals and shadowing [31]. Multiple
signals after reflection from obstacles arrives at receiver with
different amplitude and phase, and these signals causes
interference as the function of frequency. This type of fading
is called as frequency selective fading. To reduce the effect of
this type of fading different spread spectrum techniques like
Location of mobile node can be estimated by calculating
distance of mobile node from three reference nodes as
explained in TOA. RTOF require moderate synchronization
between mobile node and reference node in comparison with
the TDOA and TOA. There is no need of extra hardware in
RTOF.
A hybrid technique based on RSS and RTOF is proposed in
[33] in which an algorithm is developed to reduce the effects
of multipath signals.
3.1.2 Angle of Arrival (AOA)
Angle of arrival technique requires the measurement of angle
of mobile node with respect to the reference node. The exact
or relative location of reference nodes is already known. To
locate a mobile node using AOA technique in 2-D, it is
enough to measure angle between mobile node and two
reference nodes as shown in figure 5.
The AOA is estimated by using the directional antennas or
antenna array. It is calculated by measuring the phase
difference between antenna array elements or by measuring
the power spectral density across the antenna array. The
number of reference nodes required in location estimation
using AOA is less than the TOA and TDOA. The
synchronization between mobile node and reference node is
also not required. The disadvantage of AOA is that it requires
large and complex hardware. The extra hardware required in
AOA measurement increase the size of access point and also
increases capital cost of access point. The multipath signals,
additive noise and shadowing causes error in the measurement
of AOA. The effect of these sources is already discussed in
TOA and TDOA.
Angle between mobile node M having coordinates (X, Y) and
reference node R1 having coordinate (X1, Y1) is α1 and the
angle between mobile node M and reference node R2 having
coordinate (X2, Y2) is α2. After the measurement of angle of
32
International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Seminar on Recent Advances in Wireless Networks and Communications, NWNC-2014
arrival the coordinates of mobile node (X, Y) can be given by
(27) and (28)[34].
(27)
(28)
There are two techniques based on location finger printing,
first is K-Nearest Neighbors and the second is probabilistic
estimation.
3.2.1 K-Nearest Neighbors
K-Nearest Neighbors method estimates the position of mobile
node by comparing observed tuple with the tuples in the
database. It searches for k closet matches to the observed
tuple from the database. K is a user defined constant, the
larger value of K reduces the effect of additive noise in
location estimation of mobile node. Now by averaging these k
location coordinates, the location of mobile node is estimated
by using either weighted or un-weighted K-NN. K-NN uses
two sets of data for location estimation of mobile node, first is
the set of RSS values from N access points is denoted by a
vector S = [s1, s2, s3, s4…..sN] where, s i is the measured
signal strength in dBm. Second is the set of fingerprints
denoted by a vector F = [f1, f2, f3……fN] at the position L =
[l1, l2, l3…lN]. In this method the location of mobile node is
estimated by minimizing the Euclidean distance between
observed RSS vector S and finger print vector F. The location
l of mobile node can be estimated by using following
expression [43]:
Fig. 5. Positioning based on AOA measurement
When the position of mobile node is in between the line of
sight of two reference nodes, two AOA measurements are not
sufficient, because mobile node can be located at any point on
that axis. Therefore a third AOA measurement is required to
estimate the position of mobile node. Several previous works
have been reported in the literature (e.g. [35]-[38]), in which
position estimation using more than two AOA measurements
is considered. AOA is also called as direction of arrival
(DOA).
3.2 Scene Analysis
Scene analysis is the technique in which the location
fingerprints of a scene are collected in a database and then the
position of mobile node is estimated by comparing the
measurement with the previously collected fingerprints in the
database.RSS is commonly used for location fingerprinting.
There are many systems which uses RSS for location
fingerprinting [39]-[42]. This technique is divided in two
phases: Offline and Online. In offline phase the RSS from at
least three access points is recorded and stored in to a tuple
data structure. Each set of RSS values is associated with a
known coordinate and these associated coordinate and RSS
value are stored in to a radio map. The location of mobile
node is determined by comparing the observed online tuple
with the offline tuple stored in radio map. Various estimation
methods are used to return coordinates of the location by
comparing these tuples. The accuracy of this process can be
increased by measuring more tuples in offline phase for each
location and by decreasing the distance between these location
points. The advantage of location fingerprinting is that it
doesn’t require extra hardware, it uses the already deployed
IEEE 802.11 WLAN access points and the disadvantage of
location finger printing is that the RSS can be affected by
interference, diffraction and reflection from obstructions. The
finger print database needs to be updated, if there is a change
in the infrastructure. The error in location estimation occurs
even if the number of persons and the furniture orientation is
changed because received signal strength got changed after
reflection i.e. the database needs to be updated.
(29)
The K-NN method observes finger print S is compared with
all fingerprints recorded in radio map. There is possibility that
the two location have same RSS finger prints in that case K
neighbors with minimum Euclidean distance is selected and
the estimated location is the average of these neighboring
coordinates. The accuracy of K-NN method can be increased
by increasing the number of closet neighbor (K).
A new location estimation scheme is proposed in [44] called
dynamic nearest neighbor (D-NN). In this technique the
number of closet neighbor is not constant and will be find
dynamically according to different conditions.
3.2.2 Probabilistic Estimation
In this method the probability of selection of accurate finger
print is calculated by using Euclidean distance. The location
of mobile node can be estimated exactly, if the observed
vector l and finger print vector f are same. But there may be a
chance that the observed vector is not same as location finger
print vector. So there is error between observed and correct
location fingerprint which is given by |r - fcorrect|. There is
also error between observed and incorrect neighbor finger
print which is given by |r – fneighbor|. These errors always
hold the following condition
|r - fcorrect| ≤ |r – fneighbor|
The RSS in Wi-Fi always obey Gaussian distribution when
the RSS sample size is large [45]. The mean and variance of
error for k nearest neighbors is given by
(30)
(31)
Now by using equation (29) and (30), the probability of
getting correct location X is calculated by
(32)
33
International Journal of Computer Applications® (IJCA) (0975 – 8887)
National Seminar on Recent Advances in Wireless Networks and Communications, NWNC-2014
4. CONCLUSION
This paper surveys the various location detection techniques
in Wi-Fi. Each of the location detection technique discussed
above has their own advantages and drawbacks. TOA, TDOA
and RSS are the location detection techniques that doesn’t
require extra hardware and easy to implement while AOA
location detection technique need extra hardware because it
use directional antennas or antenna array. The location finger
printing techniques provide accurate position without the need
of extra hardware. TOA/TDOA [41], TOA/RSS [42] and
TOA/AOA [43] are the examples of hybrid techniques which
use more than one parameter to estimate the position of
mobile node. Future work includes the use of determined
location in reducing handover latency in wi-fi. There is need
of some extra work in future to reduce the localization latency
of existing algorithms.The location information of a mobile
node can be used for various location based services like
location based mobile advertising, intelligent transport
system, and location sensitive billing.
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