Transcript The Expected Uncertainty of Range Free Localization

Range-Free Sensor Localization Simulations with
ROCRSSI-based Algorithm
Matt Magpayo
[email protected]
Presentation Outline
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Introduction to WSN
The Localization Problem
ROCRSSI and Signed-ROCRSSI
Implementation and Simulation Results
Future Work
Why Wireless Sensors Networks?
• WSNs involve the use of numerous small, wireless sensors that
are inexpensive and easily deployed.
• Various applications
– Habitat monitoring
• (forest fire detection, water pollutants)
– Military surveillance
• (enemy tracking, sniper detection)
– Medical care
• (smart hospitals, patient monitoring)
• Introduces Design Challenges
– Limited storage capacity, limited energy supply, limited communication
bandwidth
– All designs must take each into consideration.
WSN Research Areas
• Tracking
– Detection and tracking in a sensor network
• Routing
– Routing protocols of the sensor network.
• Localization
– Location information of sensor nodes.
Localization
• Solution #1: Marking the location of each node as
deployed
– Impractical for large number of nodes, limited mobility
• Solution #2: GPS capabilities on all nodes
– Expensive and more energy consumption
• Solution #3: Anchor Nodes
– Have a small subset of nodes have GPS. Sensors use them
to find relative location.
• Using Ranged-Based and Ranged-Free schemes
Range-Based Localization
• Distance estimation
– Time of Arrival (TOA)
• measure signal propagation time to obtain range information
– Angel of Arrival (AOA)
• estimate and map relative angles between anchors
– Received Signal Strength Indicator (RSSI)
• use theoretical or empirical model to translate signal strength into
distance (RADAR, SpotOn)
• Distance estimation done by
• Most methods require complex hardware.
Ranged-Free Localization
• Never tries to estimate the absolute point-to-point distance.
• Some available solutions:
– Centroid Algorithm
• After receiving location information of several anchors node, use centroid
formula to estimate its location
– DV-HOP
• Anchor node flood their location and hop count throughout the network.
Nodes calculate their position based on the received anchor location, hop
count and average-distance per hop.
– Ring Overlapping based on Comparison of Received Signal Strength
Indicator (ROCRSSI)
• Reduces location of sensor to a ring of finite definite thickness by
comparing RSSI values.
Summary of ROCRSSI
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Ring Overlapping based on Comparison of
Received Signal Strength Indicator
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Basic Procedure
1.
Reduces location of sensor to a rings of
finite definite thickness.
2.
Adds rings to grid. (increments counter
in these positions).
3.
Takes region of grid with highest values.
4.
Center of gravity of region = sensor
location.
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All the sensor needs
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a list of its neighboring anchors and
relative RSSI, and, for each anchor in that
list, a list of their neighboring anchors
and relative RSSI.
Does not require sensor nodes to send out
control messages
Improving ROCRSSI : (Signed-ROCRSSI)
• Improvement
• Adding of rings to the grid where sensor cannot be (negative rings)
• Original Algorithm
• Allowing Negative Rings
Implementation and Simulation
• TinyOs and TOSSIM
– NesC programming
– Lacked signal strength simulation
• OMNet++ : Mobility Framework
– C++ programming
– Open source network simulator
– Layer by layer implementation
Simulation Timeline
1.
All anchors send a broadcast message with its location.
2.
Other anchors upon receiving broadcast messages, store the
locations and RSSI of the message in a list of their
neighboring anchors.
3.
After a predetermined interval of time, each anchor then
broadcast its location, and its list of neighbors and RSSIs.
4.
This broadcast is heard from sensor nodes, received, and
used to compute its location.
Preliminary Simulations
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Real loc = [ 350 , 250 ]
• Estimated loc = [ 374 , 258 ]
Sensor #
Real
Location
Estimated
Location
0
350,250
331,257
1
450,200
457,195
2
375,150
374,152
3
375,275
331,257
4
180,250
170,220
5
300,200
299,192
6
550,200
516,165
First extensive simulation
• Ten simulations
• 15 anchor nodes and
45 sensor nodes
randomly placed in a
2000x2000 playground
• Error Percentage =
(distance error/sensor
radio distance)
• Poor results; increase
in error
Grid Scan Algorithm and Negative
Rings
• Increase of error must be attributed to the grid scan portion of the
algorithm.
– Highest block sum approach
• High negative values near or around the area of intersection can
throw off the grid scan, causing the algorithm to search elsewhere
Alleviating shifting
• No degrees of exclusion
• Once ALL rings were added to the grid.
Negative values are taken as zero.
• Ten simulations of random placement were performed again and the
results recorded.
• However an improvement from the first set of simulations, no
overall improvement.
• Not a lot of negative rings produced.
Further simulations
• #Anchors/#Sensors
Error Percentage Varying # Anchors / # Sensors Ratio
• Overall increase in
• Spike in Centriod at
60%.
This could be
attributed to the
shifting of a centroid
that an additional
anchor provides,
ruining an otherwise
accurate estimation.
40.00%
Distance Error / Sensor Radio Range
accuracy with more
anchors.
45.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
0.00%
10.00%
20.00%
30.00%
40.00%
# Anchors / # Sensors Ratio
S-ROCRSSI
Centroid
ROCRSSI
50.00%
60.00%
Average Number of Neighboring
Anchors
• Overall increase
• ROCRSSI and
S-ROCRSSI
significantly
better than
Centroid
45.00%
Distance Error / Sensor Radio Range
in accuracy with
more
neighboring
anchors
Error Precentage Varying Avg # of Neighboring Anchors
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
3
4
5
6
7
Avg # of Neighboring Anchors
S-ROCRSSI
Centroid
ROCRSSI
8
9
Varying Anchor Placement
• Simulations on how anchor topology
effects the estimation accuracy
• Overall decrease in accuracy
• S-ROCRSSI outperforms by 20%
• Negative Rings Produced 88% of
the time
Result Summary
• Lack of improvement to estimation accuracy in many cases.
– lack of cases where the information negative rings gave actually came
into use
– Usually the negative rings only reinforced information that the original
ROCRSSI algorithm already knew.
• Substantial Difference in Unattractive Topologies
– Where the negative rings actually made a substantial difference was
when anchors were not placed along the perimeter.
– This caused a large amount of negative rings to be produced, giving the
S-ROCRSSI algorithm more information and a better location estimate.
• Sensor nodes situated outside the perimeter of the anchor nodes,
will obtain a more accurate location estimation using the S-ROCRSSI
algorithm.
Conclusion / Possible Future Work
• Despite the lack of improvement in some cases, the project did still
demonstrate the effectiveness of the ROCRSSI algorithm.
– A 16% estimation error percentage is better than most range-based
approaches out there.
• This project also helped uncover an improving algorithm for sensor
nodes located outside the anchor perimeter
• Test algorithms with actual motes in real world conditions.
• The sensor node could alternate which location algorithm it uses by
somehow estimating its general location in respect to the perimeter
of the network anchor nodes.
Questions?