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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 3, March 2011
A review on distance measurement and
localization in wireless sensor network
Kavindra Kumar Ahirwar Dr.Sanjeev Sharma(Head of department)
School Of Information Technology School Of Information Technology
Rajiv Gandhi Technical University Rajiv Gandhi Technical University
Bhopal (MP), India Bhopal (MP), India
Kavi.chak@gmail.com sanjeev@rgtu.net
Abstract -Localization is the most crucial issue in wireless sensor presents a review and taxonomy of localization methods for
network for operations and various application like tracking, mobile wireless sensor networks based on Zigbee.
positioning, monitoring, routing. This paper focuses on study of
location estimation for short range low power wireless sensor
network (IEEE802.15.4 standard) based on RSSI and LQI. This
II. LOCALIZATION[5 6 7]
review paper is divided into three sections for detailed study. First One of the most key challenges for MWSNs is the need for
section gives the overview, problem definition and taxonomy of localization. Localization is an estimation of position of sensor
localization. Second section studies the distance measurement and node in terms of coordinate system with respect of time,
computation based on signal strength. Last section describes two distance, received signal strength, time difference of arrival,
localization methods ML (maximum likehood) and WCL (weighted
angle of arrivals, theses values to locate the position of a
centroaid localization) which is mostly used in ZigBee standard
compatible to IEEE 802.15.4 standard. unknown sensor node. For many applications like habitat
monitoring, smart buildings, failure detection, target tracking
Keywords- Localization, ZigBee, Path loss model, ML, WCL. and military applications. It is more important to retrieve the
position nodes with respect to a globally recognized
I. INTRODUCTION coordinate system in order to record the data that is
geographically useful and correct.. In this review term anchor
Recent advance in wireless communication and electronics or reference node means whose location is already known,
which enabled the development of low cost System on chips which help to identify location of the unknown node.
(SoC) which implements low power, multifunctional, radio Generally these reference nodes is equipped with GPS or
transceiver and several sensors called sensor node whose size manually deployed for extract the location information. A
is small and tiny. A WSN [1,2] typically consists of a large conceptual framework of the sensor localization classification
number of these tiny sensor nodes that are deployed in a is shown in Fig.1 It shows various types of localization
region of interest. They communicate over a short distance via method. Localization schemes can also be characterized by a
a wireless medium(RF) with key feature of Adhoc deployment set of feature pairs. The schemes differ from one another in the
of sensor nodes and collaborate to achieve common way the inputs are collected. The various format of input feed
applications [3] like General Engineering, Agriculture and into localization model for mathematical computational, it
Environmental Monitoring, Civil Engineering, Military gives position in terms of coordinate and shows performance
Applications, Health Monitoring and Surgery. Current WSNs according various parameters like error, complexity etc .The
are deployed on land, underground, and underwater. nodes could be static or mobile, deployed indoor or outdoor, in
Depending on the environment, a sensor network faces a 2-D or a 3-D space. Location measurements may or may not
different challenges and constraints [4]. There are five types require additional hardware. The use of additional hardware in
of WSNs: a node should be avoided, as it not only raises the cost, but
1. Terrestrial WSN increases both form factor and operational resource
2. Underground WSN, requirements. Furthermore, a localization approaches may be
3. Underwater WSN, centralized or distributed according to the nature of the
4. Multi-media WSN, and underlying algorithm. A centralized localization algorithm
5. Mobile WSN runs on a base station which may be a coordinator node and all
participating nodes must forward their processed data to this
A class of WSN where the sensors node are moving around central node. The advantage of the centralized approach is an
physical environment ia called Mobile WSN. It can sense and
algorithm can
deployed like static node. Challenges in mobile wireless sensor
be designed that as more accuracy, precision, and can
network are self organization of topology, configuration,
coverage, energy, maintenance, and data. process. This paper process greater amounts of data. On the other hand Distributed
localization methods do not require centralized computation,
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 3, March 2011
and each node is capable of computing its location with only Some range based localization scheme are Centroid method
limited communication with nearby nodes. According to [11], each node estimates its location by calculating the center
scenario nodes are deployed indoor and outdoor region. of the locations of all seeds it hears ,APIT[12] which employs
a novel area-based approach to perform location estimation by
We categorize the localization problems into isolating the environment into triangular regions between
mainly two groups: Range Based and Range Free. beaconing nodes or anchor nodes. Some other methods
proposed in Bounding box [13], DV-HOP [14] which are
range based
III. LOCALIZATION IN WIRELESS SENSOR NETWORK
STANDARD IEEE802.15.4
IEEE 802.15.4[15] standard is to define the PHY and
MAC specifications for low data rate wireless personal area
network which are portable and mobile devices with very
limited battery consumption requirements typically operated in
indoor environment around 10 meter. Further, the purpose of
the developing this standard for ultra low complexity, ultra low
cost, ultra low power consumption and low data rate wireless
connectivity among inexpensive devices. The maximum and
minimum raw data rates were later raised to 250 and 20 kb/s,
respectively. The IEEE 802.15.4 supports the star and peer to
peer connections and it support wide variety of network
topologies.
In this section paper studies the path loss model for
distance measurement in basis of RSSI of LQI.
A. Distance measurement
This is the most fundamental issue in sensor
network for localization purpose which uses RSSI based
methods. The use of received signal strength intensity
Figure 1 CLASSIFICATION OF LOCALIZATION (RSSI)[16] is one of the most common method for localization
purposes. In contrast, RSS is one of the simplest methods of
distance measurement and can be easily implemented in real
systems. This technique is based on a standard feature found in
most wireless devices, a received signal strength indicator
A. Range Based method (RSSI). It is attractive because it requires no additional
hardware, and is unlikely to significantly impact local power
Range based scheme calculate absolute distance between two
consumption, sensor size and cost. In WSN standard RSSI and
nodes by using RSSI [8], ToA , TDoA [9] and AoA [10].
LQI is directly calculated without any need of hardware.
After distances or angles have been measured, they can be RSSI-based localization in sensor network[17,18,19] currently
used to compute locations of nodes without location has been widely used in personnel mine targeting , home
information (called unknown nodes).These method provide network control , hospital patient care , ecological environment
better accuracy for calculating node position. Nodes are monitoring , children tracking , and so on. The principle of
equipped with radios for capturing the signal strength. In this RSSI ranging describes the relationship between transmitted
paper RSSI is discussed in detail. ToA and TDoA is based on power and received power of wireless signals and the distance
time where propagation time can directly be translated in a among nodes. For modeling RSSI and distance there exists
distance, based on the known propagation speed. These several model but this paper studies two models these are free
methods can be applied to many different signals, such as RF, space path loss model and long normal model.
acoustic, infrared and ultrasound ones. While in AoA method
node measures the angle of incoming signal by using antenna B. Free space pathloss model
array or compass. Ranges based suffer from requiring extra When an antenna that emits a signal uniformly in all
hardware and environmental condition. directions without any physical interference or obstacle called
.B. Range Free Method free space. In free space, the signal power at distance d from
the antenna is proportional to where is the signal
In these schemes, unknown nodes collect location power at the antenna. If the distance from the antenna is
information from neighboring anchor nodes (nodes withknown doubled, the signal power will be reduced by a factor of four.
locations, also called anchors) and estimate their own The signal power at distance d is also a function of frequency.
locations based on their anchor nodes location information.
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In free space, the signal power at distance d can be calculated parameter self adjusting according application it provided on
from the path-loss equation (1) is [23 24].
P ( d ) = P ( 0) −10*nlog10 ( f ) −10*nlog10 ( d ) + 32.44 D. Link quality indicator
(1) The LQI (25) measurement is a characterization of the
strength and/or data quality of a received packet. The
P(d)= signal power (in dBm) at distance d, P(0)= signal IEE802.15.4 standard (IEEE Computer Society, 2006)
power (in dBm) at zero distance from the antenna, f=signal indicates that, for each received packet, the LQI shall be
frequency in MHz ,d = distance (in meters) from the antenna measured and represented as an integer ranging from 0 to 255.
n= path loss exponent , it is dependent on real environment for The minimum and maximum LQI values are associated with
free space n is 2 . Free space model is normally used when the the lowest and highest signal qualities detectable by the
transmission distance is much larger than the antenna size and receiver. The before mentioned influences during transmission
the carrier wavelength and there are no obstacles between the
of radio packets reduce the quality of RSSI extremely. Thus,
transmitters and the receivers.
localization of unknowns becomes imprecise. Another method
to determine the distance is based on the link quality indicator
C. Log-normal shadowing model
(LQI) of the transmission. It represents a number of required
It is a most commonly used propagation model that retransmissions to receive one radio packet correctly at the
considers the shadowing effect, whether in outdoor or indoor receiver. For better understanding of these parameter a table is
environment. This model indicates that the average received provided given below with some influencing parameter.
signal strength decreases logarithmically with distance. It is
also called log normal model .The calculation formula is as
follows: TABLE I. RELATIONSHIP BETWEEN LQI AND RSSI
di , j
P i , j (d ) [ dBm] = p0, j [ dBm] − 10*n *log10 ( ) + Xσ
d0 Parameters result
Signal noise RSSI LQI
(2)
Weak absence low high
P i , j (d ) = It represents the RSS at a receiving node I from weak
strong
present
absence
low
high
low
high
a transmitting node j in dB mill watts strong present high low
p0, j = the received power in dB at a reference distance The LQI sometimes generated from a signal level
from the transmitter j .It is considered from earth which assume determination, a signal-to-noise determination, or a
1 meter from earth combination of the two, at the discretion of the network node
implementer. This enables both received signal strength
n= path loss exponent
indication (RSSI) and correlation-based signal quality
estimators to be used. The LQI measurement is performed for
X σ = is the random noise variables which mean is zero, the each received packet, and the result is reported to the MAC
standard deviation is commonly 4-10. It reflects the change of sub layer as an integer ranging from 0 to 255.
the received signal power in certain distance.(It is also in dB)
By using above equation we obtain the relationship between
E. Processing RSSI values
RSSI and distance [20]
This section studies the error correcting
Usually d 0 =1 meter
model [26 27] for getting smooth value of RSSI. When
RSSI = −(−10* n *log10 (di , j ) − A ) adopting distance measurement based on RSSI, we must avoid
The theoretical distance between nodes is given by the instability of RSSI, so that the RSSI value can reflect the
distance of wireless signal transmission more accurately
RSSI − A + X σ
because of their randomness in radio signal. In this section it
d i , j = 10 10*n
studies the two experimental models for getting smoothness in
RSSI value.
In practical application environment, path loss model having
(a) Statistical Mean Value Model (SMVM)-In this model
problem with multi-diameters, diffraction, obstacle and so on
unknown node receives a group of RSSI values and then
have an impact on the wireless signal transmission[21 22]
computers their mean value for obtain optimal RSSI-value
.However it is indispensable to adjust A , n and X σ in the per distance. the formula is shown as
model according to specific environment. In general, the
1 m
∑ RSSIi
model's parameters are set based on experiences, so it does not
have the self-adaptability. For better modeling of these RSSI =
m i =1
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 3, March 2011
m is the number of the RSSI values which the
unknown node receives. By adjusting the value of m, this
model can balance timeliness and accuracy. When m is very
big, this model can avoid instability of RSSI but
communication overhead will correspondingly be high.
(b) Gauss Model (GM) - When an unknown node receives n
RSSI values, there must be some values which are small
probability events. This value saved in array A[i].It use
Gaussian distribution functions which are shown in equations
to deal with the RSSI values.
( x - m )2
−
1 2σ 2
F ( x) = e (3)
σ 2π
Figure 2- Relationship between RSSI and distance
n
1
σ2 = ∑ ( x − M )2
n − 1 i =1
(4)
IV. LOCALIZATION ALGORITHIM
1 n
M = ∑ xi (5)
n i =1 Maximum likelihood estimation [28] - It can be used when
number of reference or beacon node is equal or greater then
The RSSI value which satisfies the inequality P F(x) 1 will 3.The essential idea is: if the coordinates of n reference or
be stored in the array of A[i]. According to the practical anchor nodes were known as
experience, we choose P as the critical point which shows the ( x1 , y1 ), ( x2 , y2 ).............( xn , yn ) (7)
probability event. When the value of Gaussian distribution Their distance from unknown node D (calculated from RSSI)
function is bigger than P, we figure that the corresponding
RSSI values are high probability events, Otherwise we figure where d1 , d 2 ,........d n and then the following formula with
that the corresponding RSSI values are small probability coordinate (x, y) of the blind node D exists :
events. Generally value of P is set 0.5 or 0.6. The mean value
of all optimized RSSI values can be calculated using the ( x1 − x) 2 + ( y1 − y ) 2 = d12
formula as follows (8)
1 n ( x2 − x) 2 + ( y2 − y ) 2 = d 2 2
RSSI = ∑ A[i ] (6) Let the first n−1 equations minus the last
n i =1
equation in turn, and then we can obtain
For getting better result of RSSI value, it has to need impose a
threshold value this is called receiver sensitivity -85dB to -
90dB. x1 − xn 2 − 2( x1 − xn ) + y1 − 2( y1 − yn ) = d12 − d 2 2
xn−12 − xn2 − 2(xn−1 − xn )x + yn−12 − yn2 − 2( yn−1 − yn ) y (9)
F. Relationship between RSSI and distance = d n −12 − d 2 n
In this section it analyzes the relationship between
The above equations can then be written as the linear type: AX
RSSI and distance between two nodes considering indoor
= b, where A=
environment.
These are the parameter which is taken in
simulation. ⎛ 2( x1 − xn ), 2( y1 − yn ) ⎞
⎜ ⎟
⎜ 2( x2 − xn ), 2( y2 − yn ) ⎟
Constant Shadowing model- It uses constant shadowing
offset whose value is 4 .In this simulator gets an input value of
attenuation in dB, which will added to every transmission at ⎜ ...... ........ ⎟
every receiver. ⎜ ⎟
Lognormal Shadowing model- It uses lognormal distribution ⎝ 2( xn −1 − xn ) x, 2( yn −1 − yn ) ⎠
for the shadowing value.
We calculate average value of each node at distance Where b=
interval. Mobility is kept none.
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Vol. 9, No. 3, March 2011
the area covered by the sensor nodes. In adaptive weighted
x − xn + y − yn + d n − d
1
2 2
1
2 2 2 2
1
centroid localization[31]path loss exponent is adaptively
estimated according to the surroundings where the target
x2 2 − xn 2 + y2 2 − yn 2 + d n 2 − d 2 2 nodes situates and targeted position is calculated using WCL.
……………………………………
xn −12 − xn 2 + yn −12 − yn 2 + d n 2 − d n −12 V. CONCLUSION
This paper dealt with distance measurement and
⎡x ⎤ localization in IEEE802.15.4 compatible sensor network based
X =⎢ ⎥
⎣ y⎦ on RSSI. RSSI in most commonly used method for
localization in this standard because of their adaptability in
At last, the coordinate of node D can be written in the both indoor and outdoor environment without any extra
following type by least mean square estimation: hardware .LQI combination with RSSI is used for
characterizing the signal quality of receiving data and give
X = ( AT A) −1 AT b better result instead of RSSI standalone. As this paper
discussed two localization model for computing coordinate
WCL is much better then ML in terms of complexity because
. Weighted Centroid method [29]- The centroid method is a of matrix computation. The problem with RSSI with
simple, efficient method that can be used in dense sensor combination of WCL is adaptation of path loss exponent for
networks. This method assumes that there exists a set of real time environment, In this method it is uniform .For
anchors having (i) a known position and (ii) and overlapping addressing this problem AWCL is discussed.
transmission zones. The idea of the centroid method is to
estimate the position of a target as the average value (i.e., the
geometrical centroid) of the positions of the anchor nodes in
the transmission zone of the terminal. Furthermore, the
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Vol. 9, No. 3, March 2011
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AUTHORS PROFILE
Dr. Sanjeev Sharma is an Associate Professor and
working as Head in the Department of in SOIT-
RGPV Bhopal, Madhya Pradesh, India. He is also
Deputy Registrar of RGPV University. He obtained
his PhD degree from RGPV University. He has
published over 50 National and International
Journals & conferences various papers across India
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