The Lighthouse Location System for Smart Dust Kate Hayes.

The Lighthouse Location System for

Smart DustKate 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?__SQUARESPACE_CACHEVERSION=1252336688720

Smart Dust Sensors

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)

http://www.eecs.berkeley.edu/XRG/Summary/Old.summaries/

03abstracts/warneke.1.fig.4.jpg

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/wp-content/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

NetworksKate 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://ffden-2.phys.uaf.edu/211_fall2013.web.dir/taylor_hanley/taylor_hanley/Project%20template%202/

project/project%20pics/wide3.jpg

Corner Cube Reflector (CCR)

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

https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcSlWPFTsZQXAg1SnMTPSji9Ls20UhCE_pdchP

bvTfFVdpxX9VEy4w

http://www.angelfire.com/moon2/xpascal/MoonHoax/ApolloReflectors/CornerCube.jpg

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

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

http://www.freerangekids.com/wordpress/wp-content/uploads/

2014/06/clock.jpg

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-philippine-air-force-attack-helicopters-take-

gettyimages.jpg?v=1&c=IWSAsset&k=2&d=gFr7L5pk2CL67N1wgtw

1cP6nYokJhrfw33ewrv68Xtg%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