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The Lighthouse Location
System for Smart Dust
Kate Hayes
Paper’s Topics
What is Smart Dust
How the authors modified it for their own use
What is the Lighthouse Location System
How it works
Problems
Potential fixes and limitations
Conclusion
http://i.imgur.com/1YUEGhl.jpg
Wireless Sensor Networks (WSN)
Designed to fulfill complex modeling tasks
“Consists of a large number of cooperating small-scale nodes capable of
limited computation, wireless communication, and sensing.”
Some areas of use:
Geophysical monitoring
Precision agriculture
Habitat monitoring
Military monitoring
Various business processes
WSN rely on emergent behavior which is where the behavior of the group is
much more complex than the sum of its parts
Data fusion
WSN rely on emergent behavior and this is
facilitated by data fusion
“the process of correlating individual
sensor readings originating from various
nodes into a high-level sensing result”
Localization of individual nodes is
important because of the need to fuse
their data
http://ccri.squarespace.com/storage/Data_Fusion_v3.jpg?__SQUARE
SPACE_CACHEVERSION=1252336688720
Smart Dust Sensors
http://www.eecs.berkeley.edu/XRG/Su
mmary/Old.summaries/03abstracts/war
neke.1.fig.4.jpg
Modern nodes are not too different from our
smartphones with Wi-Fi
Berkeley envisioned a much smaller specific type of
data collecting node called Smart Dust
They are roughly a cubic millimeter in size
Inexpensive and easy to deploy
Low complexity of circuits
Require use and line-of-sight of a base station
transceiver (BST)
Smart Dust components
Small battery
Solar cell
Power capacitor
Sensors
Processing unit
Corner-cube reflector (CCR)
http://farside.ph.utexas.edu/teaching/
302l/lectures/img1347.png
Smart Dust Properties
Small size – Current RF and US transducers are too large
Mobility – Move in environment via wind and air currents
Large Scale – Small size and low costs allow many to be
made and deployed
Limited Energy – Power consumption of RF and US
technology is too large
Limited Computing and Memory Resources – small size
limits the amount of circuitry available for processing
and storing data
Single Hop Network Topology – nodes do not cooperate
with their neighboring nodes unlike multiple hop
networks
No external infrastructure besides the base station
http://www.sveosvemu.com/wpcontent/uploads/tdomf/16461/vetar.jpg
Localization Challenges for Smart Dust
The Base station must know its location exactly and nodes
localize to its coordinate system
The accuracy needed from the network is determined by
what is being sensed
Dust nodes do filtering and basic processing of data
onboard so as to save energy with communication to other
nodes
Instead they communicated only to the base station
The costs involved are special, capitol, and time, all have
to be taken into account when designing the system
Nodes must know their own location!
Lighthouse Location System
Because the nodes must know their own
location for WSNs, different ways have been
suggested
This paper focuses on the lighthouse
location system
It uses a cylindrical approach instead of the
more traditional spherical scanning pattern
It relies on a parallel beam transceiver that
rotates around the cylindrical base (Like a
lighthouse, hence, the name)
Nodes are individually calibrated to the
beam
Ideal Lighthouse Location
A perfectly parallel beam that sweeps a
perfect circle with no errors
Three cylinders are mounted in the corner of
a cube perpendicularly to allow a parallel
beam to sweep in every direction
Using the relatively simplistic mathematics
described in the paper, the location of the
node can be determined
Location from measuring the time between
sweeps and how long a sweep takes
The node determines its location from
solving a equation system of the different
distances involved
Realistic Lighthouse location
Perfect parallel beams are difficult to produce, there will
usually be spread which can lead to large errors at tens of
meters away
Only the edges of a beam are required for the measurement
so two lasers mounted parallel are used instead.
This allows for less beam spread
Rotating 45 deg. Mirrors are used in order to get the upper
half of the cylinder as well as more than one plane
The math to discover the calibration and distances can be
found and fairly simply followed in the paper
Images of the Lighthouse System
Nodes in the Lighthouse System
Light is received from the BST via a photodiode
A high pass filter is used to filter out ambient light from the sun and bulbs
An interrupt is triggered when the voltage goes high or low, accomplished by a
Schmitt Trigger
There is a Linux processor processing the voltages as spikes over time in order to
calculate the different times required to calculate the distance and position
Outliers from nodal movements during the beam passing are rejected from
calibrations
Lighthouse Base Transceivers
Bases MUST be mutually perpendicular for the math and angles to work out
correctly
The offset in position of the bases must be known
Once placed in the field they will be calibrated
Inherent errors in the system
Mirror vibrations from fast rotation of the base
Time of mirror rotation is limited to a
resolution correlating to the speed of rotation
The rotation platform may flutter and cause
slight jiggles in the beam
Hardware delays from slow or lagging circuitry
and/or processing
Clock resolution limits how fast the beam can
rotate without being inaccurate
The clock can drift
Errors are dominated by vibration, time of
mirror and flutter.
Conclusion
Base Stations sweep out parallel lasers in all directions in order to
allow nodes to calibrate their location and send information back to
the system
Smart Dust nodes are tiny cubes a millimeter on each side, tiny
compared to other nodes
This size imposes restrictions but also brings about new possibilities
Circuitry and processing on board the nodes is performed simply and
quickly to save resources
There are many potential uses to a network of tiny line-of-sight
sensors
Can you think of any?
StarDust: A Flexible
Architecture for Passive
Localization in Wireless
Sensor Networks
Kate Hayes
Paper’s Topics
Wireless Sensor Networks
State of current WSNs
StarDust Network
Implementation tests
Optimization Techniques
Results
Conclusion
http://apod.nasa.gov/apod/image/0711/M45WF_hallas_r800.jpg
Wireless Sensor Networks (WSN)
Envisioned to revolutionize the way humans and machines interact and
observe their environments
This paper covers the specific type of WSN that are dropped aerially and
sensors are embedded in the environment for recording
Sensor nodes form a network and collaborate to get the sensing job
completed
Uses include:
Habitat monitoring
Structural integrity monitoring
Military Surveillance
State of Current WSN Technology
No universally accepted localization problem solution
Range-based technology
Uses ranges of different nodes from each other or a base station to determine location
Often accomplished with GPS (expensive, heavy)
Time-of-Flight
Time-Difference-of-Arrival
Or Radio transmitters (large energy expenditure)
Radio Interferometry
RSSI
Range-free technology
Sensors use primarily connectivity information to infer proximity to sets of anchors
Centroid localization- distance to center beacon/anchor
APIT- being inside or outside a triangle produced by beacons
DV-hop- uses hop to hop propagation
Spotlight- well controlled events that nodes can use to determine location
Lighthouse Location System- Parallel beams are used to measure distances
StarDust Localization Model
StarDust is a Range-free solution to the
localization problem of WSNs
Designed after the universe containing luminous
bodies that reflect back light
Rather than having the SensorBalls emit light to
be captured they reflect it using a passive
optical element
Basically a bunch of SensorBalls are aerially
dropped and a flash of light and picture is taken
and the light reflected back is analyzed for
distance from base
The distance of each individual node is sent to
them so they can do their sensing tasks
http://ffden2.phys.uaf.edu/211_fall2013.web.dir/taylor_hanley/taylor_hanle
y/Project%20template%202/project/project%20pics/wide3.jpg
Corner Cube Reflector (CCR)
http://www.angelfire.com/moon2/xpascal/MoonHoax/ApolloRef
lectors/CornerCube.jpg
https://encryptedtbn2.gstatic.com/images?q=tbn:ANd9GcSlWPFTsZQXAg1SnMTPSji
9Ls20UhCE_pdchPbvTfFVdpxX9VEy4w
The angle of incoming light is not
important
CCRs reflect light back in exactly
the same way it came in
Due to unique design of three
mutually perpendicular mirrors
In StarDust model there are many
CCRs on a single SensorBall
SensorBalls are designed to be
upward orienting with the CCR at
the top so as the flashed areal light
will always be able to hit the CCRs
StarDust System Architecture
Light Emitter- Strobe light produces very intense, non-monochromatic
collimated light pulses represented by the spectral density ψ
Transfer Function- A bandpass filter for the incident light on the CCR, is also
called the color of the node
Image Processing- Collects light and determines where the nodes are located,
but not which node is which
Node ID Matching- Uses locations detected by image processing and uses
Probabilistic label relaxation to determine which node is which
Radio Model- Acts as an aid to Node ID matching by providing an estimate of
the radio range of nodes within range
Image Processing
“The goal of the Image Processing Algorithm (IPA) is to identify the location of
the nodes and their color”
It does not identify which node fell where, but only the location of nodes
Records two pictures, one in which the deployment area is illuminated and
one not illuminated
The difference between the dark and light is found to create a filter
This filter image then goes through several transformations to remove
features that were present in both the light and dark image
In order to identify the elements in the filter that are the reflected light, an
intensity filter is applied to the filtered image creating a grayscale image
From the grayscale it is fairly easy to determine that the brightest objects are
the SensorBalls
Once the nodes are identified, an edge detection program on Matlab is run so
that the centroid can be computed to get the exact location of the nodes
Node ID Matching
Node ID Matching’s goal is to match each bright spot
calculated with the IPA with an actual specific node
Node IDs are also referred to as the node’s labels
The problem of labeling a specific node located on the
image processed grid with a label is modeled with a
technique called probabilistic label relaxation
The main idea is to iteratively compute the probability a
label belongs with a specific node using the support for
different labels
StarDust uses four types of label relaxation support
constraints
Color constraints
Connectivity Constraints
Time Constraints
Space Constraints
Probabilistic Label Relaxation [1]
Often used for solution of simultaneous nonlinear equations
Features such as edges, points, or surfaces belong to a set of labels and an
object
Label schemes tend to be probabilistic in nature
Weights or probabilities are assigned to each label in the set giving an
estimate of the likelihood that the particular label is the correct one for that
feature
The individual probabilities are then iterated through many times taking using
a probabilistic approach taking into account neighboring probabilities until
they converge or fail to converge
When they fail to converge the user is left with the probability that the
feature has a certain label
Relaxation with Color Constraint
The mapping between a sensor node’s position and a label
can be obtained by assigning a unique color © assigned to
each node
The IPA can determine color
Obviously this is limited to the number of colored CCRs
that can be obtained
If C=0 no specific node can be identified using just this
constraint
If C>1 a color is assigned to specific nodes giving them the
status of “anchor” node
http://www.w3schools.com/tags/colormap.gif
Relaxation with Connectivity Constraint
Connectivity information between the nodes can be obtained through the
network through beaconing and assist in labeling the nodes
After deployment there is set of beaconing “Hello” messages sent to each
node and from these messages the node builds a table of its neighbor’s
information
Each node sends back its neighbor table to the central device
Each node is assigned every possible label with an initial probability
The neighbor tables are used help iterate through every possibility using the
relaxation technique
These probabilities are iteratively updated when the consideration of their
interaction with radio range is taken into account for large scale networks
Relaxation with Time Constraint
http://www.freerangekids.com/word
press/wpcontent/uploads/2014/06/clock.jpg
Time constraints can be treated similar to color
restraints
The most simplistic case is for one SensorBall to be
dropped at a time
The IPA is run and the one new flash of light is
obviously the ball just deployed
It is too impractically in terms of time for large or
medium scaled networks so it is unlikely just a time
constraint can be used as a localization technique
Relaxation with Space Constraint
Information about the space the node is dropped
in compared to other nodes is another constraint
There is the location of the node and location of
the label (where the node was launched vs. where
it landed)
At the exact time of release these two locations
are identical
If the most simplistic model of physics was used it
would be fairly simple to calculate where to the
node landed
Instead wind and other conditions need to be
accounted for
It is complex but can be done
The spatial constraint is achieved by recursively
assigning the probability a node has a certain label
using the distances between the location of a node
with multiple nodal locations
The nearest label is not always correct, it is
dependent on drop and environment conditions
The more space between drops, the higher the
accuracy of the method
http://cache1.asset-cache.net/gc/52154971-philippineair-force-attack-helicopters-takegettyimages.jpg?v=1&c=IWSAsset&k=2&d=gFr7L5pk2CL67
N1wgtw1cP6nYokJhrfw33ewrv68Xtg%3D
Relaxation Techniques Analysis
Energy consumed is the overhead
Network Size is the scale
N = number of nodes
ε_d = energy spent for one areal drop
ε_b = energy spent in the network for collecting and reporting neighbor
information
T_d = time taken by a sensor node to reach the ground
Testing
Tests were carried out with various aspects
of the entire StarDust localization scheme
being tested
Image Processing Test
Node ID testing
Radio Model
Localization error vs. coloring space size
Localization error vs. color uniqueness
Localization error vs. connectivity
Image processing algorithm vs Localization
test
Localization Time of different relaxation tests
Image Processing Test
In the pictures in the above slide there are 6 sensor nodes
Different sets of pictures were taken with different angles and zoom of the
camera
These images were processed according to the IPA mentioned earlier using
Matlab
Nodal ID Radio Model Test
Average number of beacons
is procured for low and high
connectivity networks
Low connectivity has half the
amount of beacons as high
connectivity
Results are in good
agreement with the
predicted radio model
Nodal ID Localization Error vs. Coloring
Space Size Test
The effect of the number of
colors on localization accuracy is
tested
Colors are randomly assigned to
the sensor node
The location algorithm is run for
three different ranges of
distance
Conclusion: A larger number of
colors available significantly
decreases localization error
Nodal ID Localization Error vs. Color
Uniqueness
A unique color gives a node the state of “anchor”
The anchor can easily and accurately be identified throughout the image processing process
Color amounts were fixed (4, 6, or 8) and the number of nodes assigned unique colors varied
from 0 - max #nodes
Localization accuracy does increase the more colors are available
The localization accuracy decreases with the amount of specific nodes that are assigned
unique colors
Nodal ID Localization Error vs.
Connectivity
A low and high connectivity network were once again used
The number of colors available was varied and there were no anchors
In both situations localization error decreased with an increase in the number
of colors as expected
Localization Error vs. Image Processing
Error from the Nodal ID matching component
was examined and now the error from the
image processing module will be examine
Luminous objects (sunlight, reflections,
streetlights, cars, etc.) can be mistaken as
nodes and are called false positives
The bigger problem is false negatives which
is when the sensor nodes fail to reflect back
enough to be detected
The localization algorithm was run with a
percentage of false negatives induced to see
the effect on the localization error
As expected the localization error goes up
with the number of false negatives recorded
Localization Time
Duration of the localization of the nodes based on the different techniques or
combinations of them
It is assumed 50 unique colored filters can be manufactured
Both the connectivity restrained and time constrained techniques increase
linearly with the network size
System Range
The realities of physically dropping
and recording the nodes is examined
The range of the localization of the
system should obviously decrease in
worsening atmospheric conditions
Light scattering limits the visibility
range by redirecting the luminance of
the source and reducing the apparent
contrast (C) between target node and
the background (r)
When C reaches its lower limit no
increase in source luminance or
receiver sensitivity can improve the
system range
System performance is drastically
reduced in hazy atmospheric
conditions
Conclusions
Four primitives for constraint based relaxation algorithms were proposed:
Color, connectivity, time, and space
Interesting research directions could be to implement more than one or two
constrain algorithms at a time or employ a voting scheme
Labeling the nodes is not highly accurate, the algorithm sometimes fails to
converge
In the future it might be possible to get readings in the environment and use
those events to help with labeling
It is possible StarDust can be used for rugged terrain and dense foliage
The location readings would be taken before the sensors disappeared from
view under the plants, but they would need self-righting capabilities in the air
StarDust solves the localization problem for areal deployment where
passiveness, low cost, small size, and rapid localization is required
References
[1]- http://www.cs.cf.ac.uk/Dave/Vision_lecture/node43.html