Transcript Lighthouse - Vanderbilt University

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