Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer...
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![Page 1: Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha Presented by Ray Lam Oct 23, 2004.](https://reader030.fdocuments.us/reader030/viewer/2022032704/56649d7e5503460f94a610ee/html5/thumbnails/1.jpg)
Dynamic Clustering forAcoustic Target Tracking inWireless Sensor Network
Wei-Peng Chen, Jennifer C. Hou, Lui Sha
Presented by Ray LamOct 23, 2004
![Page 2: Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha Presented by Ray Lam Oct 23, 2004.](https://reader030.fdocuments.us/reader030/viewer/2022032704/56649d7e5503460f94a610ee/html5/thumbnails/2.jpg)
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Outline
Introduction to sensor network Technical background for the system The dynamic clustering algorithm Limitations of the system Conclusion
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Sensor Network
Nodes in the network Sensor to sense
physical environment On-board processing,
limited capability Wireless
communication Limited power from
batteries
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The Network
The network 2 kinds of nodes:
source and sink Wireless network
Berkeley motes use CSMA MAC
Ad-hoc type Multi-hop routing Nodes sleep
periodically
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Data Dissemination
Some research questions How to coordinate sensors? How to route data? How to do in-network data fusion? What to do with congestion? How to do the above efficiently…
in terms of energy? in terms of time?
We need distributed solutions
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The Acoustic Target Tracking System
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Energy-based Localization
Signal strength decreases exponentially with propagation distance
iii xxar
: received signal strength in the ith sensor: strength of an acoustic signal from the target: target position yet to be determined: known position of the ith sensor: attenuation coefficient: white Gaussian noise
irRa2Rx2Rxi
i
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Energy-based Localization
With a pair of energy readings Target is closer to sensor i than to sensor j
ji rr
j
i
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Energy-based Localization
Voronoi diagram 2-D space divided into Vor
onoi cells V(pi): Voronoi cell containin
g node pi
V(pi) contains all points closer to pi than to any other pj
ri larger than all neighbors’ readings only if target in V(pi)
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Network Characteristics
Network structure: 2-layer hierarchyStatic backbone of sparse cluster headsDense sensors for detecting targets
Radio transmission range = 2 * signal detection rangeEnsure 1 cluster at a timeEnsure nodes in a cluster hear each other
directly
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The Dynamic Clustering Algorithm
4 component mechanisms Initial distance calibration and tabulationCluster head (CH) volunteeringSensor replyingReporting of tracking results
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Idea of the Algorithm
Objective: minimize messages sent in the network and avoid collisions
Given an energy reading, estimate distance from target
Using Voronoi diagram, estimate probability that target is in my Voronoi cell
In CH volunteering and sensor replying process Nodes with high probability speak quickly When you hear a higher energy reading from others,
you give up speaking
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Initial Distance Calibration and Tabulation Each sensor to know 2-D coordinates of al
l other sensors in its transmission range Each CH constructs a Voronoi diagram for
neighboring CHs Each sensor (including CH) constructs a V
oronoi diagram for neighboring sensors
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Initial Distance Calibration and Tabulation Each CHi pre-computes for different
d Target on the circle centered at CHi with radiu
s d : conditional probability that target locat
es within V(CHi) given d3 cases…
diPr
/1/ ard
diPr
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Three Cases
d < radius of inner circle:
d > radius of outer circle:
In between: Take sample points on the
circle Check location of each poi
nt Estimate as # of sam
ple points inside V(CHi) / total # of sample points
1Pr di
0Pr di
diPr
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Initial Distance Calibration and Tabulation Sensors do similarly Each sensor Sj pre-computes for
different ri: energy reading from CHi
rj: energy reading of Sj
: conditional probability that target locates in V(Sj) given
jirj Prjir
jiji rrr /
jirj Prjir
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CH Volunteering
Distributed election algorithm CH closest to target should be elected Solicitation packet
Request to form cluster and volunteer to be the cluster head
Contains signal signatureContains signal strength detected by CH (CHi)
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CH Volunteering
Random delay-based broadcast mechanism CHi detects a signal, estimates d, checks
Sets a back-off timer with back-off time
CHi does not broadcast solicitation packet until timer expires
If during back-off, hears other solicitation packets with higher energy readings, gives up volunteering
diPr
ranWUdiWWWD Pr1minmaxmin
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Sensor Replying
Sensor Sj receives a solicitation packet Matches signal signature with buffered dat
a Upon a match, calculates signal strength rj
Attempts to send a reply using similar delay-based mechanism
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Sensor Replying
Random delay-based broadcast mechanism Calculates , checks Sets back-off timer with back-off time
If during back-off, hears other reply packets, records the sensor that reports largest signal strength
When timer expires, sends reply packet if rj higher than all others’ energy readings; or Sj is a Voronoi neighbor of the sensor that reports the largest si
gnal strength
jir jirj Pr
ranji WUrjWWWD Pr1minmaxmin
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Reporting Tracking Results
CH receives replies from sensors Sufficient number of replies:
A reply from Sj with largest signal strength
Replies from all Sj’s Voronoi neighbors
Takes location of Sj as location of target Sends result to sink through static backbon
e
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Limitations
Limited application space Not applicable to general monitoring applications
without “target” Signals must attenuate with propagation distance
1 cluster for 1 signal Signals may come simultaneously Multiple clusters may form simultaneously causing
more collisions
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Limitations
Energy inefficiencyRadio transmission range = 2 * signal
detection rangeCan be improved by considering multi-hop
routingSignals at any position must be detected by at
lease 1 CH Tradeoff of sensor density and energy efficiency
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Conclusion
Data dissemination in sensor network Dynamic clustering triggered per signal More research on:
Collision behavior between clustersMulti-hop routingTime efficient data dissemination
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Discussion
The End
Thank you for coming!