1.
Prepared by:
Gamal Sallam
Prepared for:
Dr. Othman Baroudi
2.
What?
◦ To determine the physical coordinates of a group of sensor nodes in a
wireless sensor network (WSN)
◦ Due to application context, use of GPS is unrealistic
• GPS can work only outdoors.
• GPS receivers are too expensive for wide-range deployment.
• It cannot work in the presence of obstructions.
Why?
◦ To report data that is geographically meaningful i.e., object tracking
◦ Services such as routing rely on location information; geographic
routing protocols; context-based routing protocols, location-aware
services
◦ coverage area management
◦ Self deployment
3.
Accuracy: Different applications have different requirements
Energy constraints: All operations involved in localization and
tracking must be energy efficient
Signal interference: collisions between packets transmitted by
different nodes at the same time
Physical Layer Measurements:
- Signal strength, time of arrival, angle of arrival
- Prone to physical layer impairments (multipath
propagation, fading, shadowing, noise, etc.)
Computational Constraints:
- Sophisticated algorithms cannot be efficiently performed on
wireless sensor nodes because of processing or memory
constraints
4.
Triangulation
Finger print
Centroid localization
Next: the common follow of the triangulation
approach.
5.
Start
Exist an Unknown Node which has atExist an Unknown Node which has at
least three reference node in its
coverage area
Select an Unknown Node
Reference Node
Estimate the Distance to the
Reference Node
Select Reference Node
Any Selected Reference Node
Without Estimated Distance
Any Selected Reference Node
Without Estimated Distance
Selected Unknown Node
Calculate the Position of the
Selected Unknown Node
Unknown Nod Selection
Distance Estimation
Position Computation
End
6.
The method used for distance calculation:
i. RSSI
ii. LQI
iii. TOA
iv. TDOA
7.
Received signal strength indicator.
- The idea:
- transmission power at the transmitting device ( ) directly
affects the receiving power at the receiving device ( ).
- Using Friis’s free space transmission equation:
(1)
(2)
8.
An ideal distribution of is not applicable
in practice
In practice, the actual attenuation depends on
multipath propagation effects, reflections,
noise, etc.
These attenuation degrades the quality of the
RSSI significantly.
Realistic models replace with (n=3..5)
9.
Link quality indicator
it indicates how strong the communications
link is.
based on the received signal strength as well
as the number of errors received.
It is only made available by IEEE 802.15.4
compliant devices.
10.
Distance between sender and receiver of a signal can be
determined using the measured signal propagation time
and known signal velocity
Sound waves: 343m/s, i.e., approx. 30ms to travel 10m
Radio signals: 300km/s, i.e., approx. 30ns to travel
10m
One-way ToA
one-way propagation of signal
dist =(t -t )*v
11.
Two-way ToA
round-trip time of signal is measured at
sender device
requires highly accurate synchronization of
sender and receiver clocks
12.
two radio signals travelling at different speeds such as
radio frequency (RF) and ultrasound.
example: radio signal (sent at and received at ),
followed by acoustic signal (sent at and received at
)
+ve: no clock synchronization
required
+ve: distance measurements can be very accurate
-ve: need for additional hardware
13.
Range-based uses absolute point to-point
distance estimates for calculating the
location.
more expensive
Better accuracy
Range-free doesn’t need such assumption.
It assume that hop count proportional to the
their distance (less realistic)
cost-effective
Less accuracy
14.
In centralized algorithms,
• nodes send data to a central location where computation is
performed and the location of each node is determined
and sent back to the nodes.
In distributed algorithms,
• each node determines its location by communication
with its neighboring nodes
• robust and energy efficient
15.
Centralized:
expensive because the power supply for each
node is limited.
latency, as well as consuming network
bandwidth.
Decentralized
reduce the power-consumption
Can be more complex to implement
At times may not be possible due to the limited
computational capabilities of sensor nodes
17.
determine the location of a target point by
measuring distances to it from three different
known points.
Step 1: distribute the beacon
nodes in the area of interest;
Step 2: determine the distance
between each beacon node and
the target node d1,d2, and d3
based on the RSSI, LQI, ToA, or
TDoA values;
Step 3: calculate the
intersection point (the target
node) between the three beacon
nodes with radiuses d1, d2, d3.
18.
We have the following three equations:
Solve the above equations to get x, y.
Problem: d1,d2, and d3 will never be
sufficiently accurate.
19.
Divide the area of interest in grids.
determining how the signals will be received at every grid point.
Two phases: offline phase& online phase.
Offline phase:
Step 1: distribute the beacon nodes , , in the area of tracking;
Step 2: divide the area of tracking into several small grids and use the
grid points as reference points (x, y) , (x, y) ,. (x, y) , …in the tracking
area;
Step 3: get the RSS values at each reference point from beacon nodes
and store them in the DB with the corresponding locations coordinates.
Online phase:
Step 1: the mobile target enters the tracking area, and then collects the
RSS values from each beacon node;
Step 2: compares the collected RSS values with the stored values in the
DB;
Step 3: retrieve the position from the DB with the closest RSS values.
20.
Pros:
Better accuracy
Less computation overhead on sensor
Cons:
Collecting RSS values and send them to the
server requires long period of time especially
if the area is large.
the searching procedure through the
stored samples is time consuming.
21.
relies on a high density of beacons.
every target sensor node can hear from several
beacons.
each target node estimates its location by
measuring the centre of the location of all nodes
it hears.
all beacons send their
position , ( = 1 … , )
to all target sensor nodes
within their transmission
range.
22.
Then all target sensor nodes calculate their
own position (x, y) by averaging the
coordinates of all n positions of the beacons
in range.
23.
Introduces weight functions to improve the accuracy of
localization.
depends on the distance and the characteristics of the
target node receivers.
g depends on the application scenario.
24.
each node maintains a table { , , ℎ } (location of anchor node
i and distance in hops between this node and anchor node i).
when an anchor obtains distances to other anchors, it
determines the average hop length (“correction factor” ),
which is then propagated throughout the network.
given the correction factor and the anchor locations, a node
can perform trilateration by multiply ℎ *c.
25.
Calculate c:
C(a1)=100+40/(6+2)=17.5
C(a2)=(40+75)/(2+5)=16.42.
C(a3)=(75+100)/(5+6)=16.42.
Each anchor send its c value.
Node n will receive first from
A2, and will consider it the avg
Distance per hop.
so the distance from anchors to node n is calculated by multiplying the
minimum hop number and received c.
n−>a1=3∗16.42=49.26,
n−>a2=2∗16.42=32.84,
n−>a3=3∗16.42=49.26.
Then use triangulation to compute node n position
If nodes are randomly distributed DV-HOP results in a large
localization error.
26.
The uncertainty of the distance determinations due to the
changed application circumstance and the nature of radio
signal propagation.
Environment Factor
Eliminating the Outliers of Radio Signals
Evolutionary Optimization
27.
The tracking environment in which a target is located is, in most
cases, dynamic, i.e., people waking in an indoor environment, or
weather changes in an outdoor environment.
computes the environmental factors between beacon nodes with
known positions, based on finding out the relationship between
distances and RSS values.
28.
The environment factor can be measured between each
beacon node pair and
The average environmental factor μ can be introduced as
the main characteristics for the tracking environment.
Where n is the total number of beacon node covering the
mobile target MT.
29.
Each mobile target receives at least three different factors from
beacon nodes, in addition to the RSS values for each beacon node.
It compute the average environment factor μ .
Compute the distance using this equation:
Then use triangulation to
Calculate the position.
30.
RSSI and LQI are affected by many environment factors such
as reflections, obstacle, and other electro-magnetic fields.
Eliminating noise elements will assist in improving the
accuracy of the localization.
The Dixon method is used here to eliminate the outlier of
RSSI values.
The standard deviation of all the RSSI values received each
time is recorded as .
The standard deviation threshold is defined as .
The RSSI value, noted as , obtained from the RSSI
measurement is as follows:
31.
m is the number of the RSSI values which are less than or
equal to the mean of q RSSI values, alpha is calculated
according to the following equation:
32.
In the absence of noise in a system, the intersection of the
circles determines the one and only one target position.
But it yields ambiguous solutions in the presence of noise in the
system, since the circles may intersect at multiple points
due to erroneous distance determination.
Consequently, the localization problem
becomes a searching problem.
the location of the target node
is calculated as follows.
A popular statistical localization algorithm
is the nonlinear least squares (NLS) techniques
33.
PSO is a new heuristic method inspired by the social behavior of bird
flocking.
particles fly through the problem hyperspace with given velocities.
At each iteration, the velocities of the individual particles are
stochastically adjusted according to the historical best position for
the particle itself (pBest) and the overall swarm best position (gBest).
Both pBest and gBest are derived according to a user defined fitness
function.
The fitness function can be defined as follows:
where
the searching space of the blind node can be defined as follow:
34.
Where ( , ) is the coordinates of the ith reference node;
is the measured distance between the blind node and the ith
reference node;
is the maximum range error of TOF ranging engine in the
tunnel environment;
N≥3 is the number of the selected reference nodes.
Then the rectangle defined by ( , ),( ,, ) is the
searching space of the blind node.
The particles of PSO are randomly initialized in the searching
space at the beginning:
Where ( , ) is the position of the particle, rand(1) generates a
random number with a range of [0,1] and M is the number of the
particles.
35.
Each particle updates its position based on its own best exploration, the best swarm
overall experience and its previous velocity according to the following model:
Where ( , ( )) is the current velocity vector of particle j;
while ( + 1 , ( + 1)) is the velocity vector of particle j for the next iteration;
( , ( )) is the current position of particle j;
( + 1 , ( + 1)) is the position of particle j of the next iteration;
(pBest , pBest ( )) is the best position particle j achieved based on its own
experience during previous k iterations;
(gBest , gBest ( )) is the best particle position based on over swarm’s experience
during previous k iteration; w is the inertia weight; , are two positive constants;
rand(1) is a randomly generated number with a range of [0, 1]; and k is the iteration
index.
36.
Challenges:
The space shape is long and narrow: WSN deployed there is of
the line or chain type and has low density, and data
transmission is energy expensive because of the multiple
hops;
The air is wet and dirty due to water and dust, which
significantly affects the valid wireless communication
distance.
The surface is usually rough and the multi-path effect on
radio propagation is severe.
37.
Population 10,
Max iteration 200
c1and c2 1.494,
w 0.729
Satisfied fitness value 1
38.
linear least square estimation (LLSE).
seven potential estimation (SPE)
particle swarm optimization estimation (PSOE)
39.
how to enable enough beacons in the neighborhood and if
there are not enough beacons, there how to use some of the
mobile target nodes whose locations have been determined
as additional beacons.
40.
Mobile target node 1 (Class A) contains three beacons in its
range and can get high accuracy and can be used as a
reference node.
Mobile target node 2 is covered only by 2 beacon nodes with
known position, and one mobile target node with previously
determined position, less accuracy.
Class C offers the worst tracking accuracy as the mobile
target nodes is covered by only a single beacon nodes and
the rest of the available reference nodes are the mobile target
nodes with previously determined positions.
The error will be accumulated in Classes B and C.
41.
Service Industry:
robots that perform tasks such as basic patient care in nursing
homes, maintenance and security in office buildings.
Requires a mechanism for position estimation.
Skilligent uses a visual localization system based on pattern
matching.
Pollution Monitoring
Sensor nodes that measure specific pollutants in the air are
mounted on vehicles.
As the vehicles move along the roadways, the sensors sample the
air, and record the concentration of various pollutants along with
location and time.
When the sensors are in the proximity of access points, the data
are uploaded to a server and published on the web.
42.
Shooter Detection / Weapon Classification:
a soldier-wearable sensor system is developed that not only
identifies the location of an enemy sniper, but also identifies the
weapon being fired.
Each sensor consists of an array of microphones mounted on the
helmet of a soldier.
The sensor observes both the shock wave of the projectile, as
well as the muzzle blast from the weapon, and based on TDOA,
as well as properties of the acoustic signal, is able to triangulate
the enemy position and classify the weapon type.
Pothole Detection:
a system is developed to detect potholes on city streets.
Deployed on taxi cabs, the sensor nodes contain an
accelerometer, and can communicate using either opportunistic
Wi-Fi or cellular networks.
43.
1. Shuang-Hua Yang,” Wireless Sensor Networks Principles, Design and
Applications”, chapter 10, Springer, 2014.
2. Tareq Alhmiedat, “Tracking Mobile Targets through Wireless Sensor
Networks”, A Doctoral Thesis, Oct, 2009.
3. Qin Y., Wang F., Zhou, C., Yang, S.H.: A particle swarm optimization
based distributed localization scheme in tunnel environment. Wireless
Sensor Systems—IET Conference, June,London (2012) .
4. M. Keshtgary, M. Fasihy, and Z. Ronaghi,” Performance Evaluation of
Hop-Based Range-Free Localization
MethodsinWirelessSensorNetworks” International Scholarly Research
NetworkISRN Communications and Networking, 2011.
5. Isaac Amundson and Xenofon D. Koutsoukos, “A Survey on
Localization for Mobile Wireless Sensor Networks”, Mobile Entity
Localization and Tracking in GPS-less Environnments, Volume
5801, 2009, pp 235-254.
6. Grossmann, Ralf, et al. "Localization in Zigbee-based sensor
networks."Proceedings of 1st European ZigBee Developers Conference,
EuZDC. 2007.
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