Dynamic clustering for acoustic target tracking in wireless sensor network
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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. Outline. Introduction to sensor network Technical background for the system The dynamic clustering algorithm Limitations of the system

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Dynamic clustering for acoustic target tracking in wireless sensor network

Dynamic Clustering forAcoustic Target Tracking inWireless Sensor Network

Wei-Peng Chen, Jennifer C. Hou, Lui Sha

Presented by Ray Lam

Oct 23, 2004


Outline
Outline

  • Introduction to sensor network

  • Technical background for the system

  • The dynamic clustering algorithm

  • Limitations of the system

  • Conclusion


Sensor network
Sensor Network

  • Nodes in the network

    • Sensor to sense physical environment

    • On-board processing, limited capability

    • Wireless communication

    • Limited power from batteries


The network
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


Data dissemination
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



Energy based localization
Energy-based Localization

  • Signal strength decreases exponentially with propagation distance

: 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


Energy based localization1
Energy-based Localization

  • With a pair of energy readings

    • Target is closer to sensor i than to sensor j

j

i


Energy based localization2
Energy-based Localization

  • Voronoi diagram

    • 2-D space divided into Voronoi cells

    • V(pi): Voronoi cell containing 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)


Network characteristics
Network Characteristics

  • Network structure: 2-layer hierarchy

    • Static backbone of sparse cluster heads

    • Dense sensors for detecting targets

  • Radio transmission range = 2 * signal detection range

    • Ensure 1 cluster at a time

    • Ensure nodes in a cluster hear each other directly


The dynamic clustering algorithm
The Dynamic Clustering Algorithm

  • 4 component mechanisms

    • Initial distance calibration and tabulation

    • Cluster head (CH) volunteering

    • Sensor replying

    • Reporting of tracking results


Idea of the algorithm
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


Initial distance calibration and tabulation
Initial Distance Calibration and Tabulation

  • Each sensor to know 2-D coordinates of all other sensors in its transmission range

  • Each CH constructs a Voronoi diagram for neighboring CHs

  • Each sensor (including CH) constructs a Voronoi diagram for neighboring sensors


Initial distance calibration and tabulation1
Initial Distance Calibration and Tabulation

  • Each CHi pre-computes for different d

    • Target on the circle centered at CHi with radius d

    • : conditional probability that target locates within V(CHi) given d

    • 3 cases…


Three cases
Three Cases

  • d< radius of inner circle:

  • d> radius of outer circle:

  • In between:

    • Take sample points on the circle

    • Check location of each point

    • Estimate as # of sample points inside V(CHi) / total # of sample points


Initial distance calibration and tabulation2
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


Ch volunteering
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 signature

    • Contains signal strength detected by CH (CHi)


Ch volunteering1
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


Sensor replying
Sensor Replying

  • Sensor Sj receives a solicitation packet

  • Matches signal signature with buffered data

  • Upon a match, calculates signal strength rj

  • Attempts to send a reply using similar delay-based mechanism


Sensor replying1
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 signal strength


Reporting tracking results
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 backbone


Limitations
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


Limitations1
Limitations

  • Energy inefficiency

    • Radio transmission range = 2 * signal detection range

    • Can be improved by considering multi-hop routing

    • Signals at any position must be detected by at lease 1 CH

      • Tradeoff of sensor density and energy efficiency


Conclusion
Conclusion

  • Data dissemination in sensor network

  • Dynamic clustering triggered per signal

  • More research on:

    • Collision behavior between clusters

    • Multi-hop routing

    • Time efficient data dissemination


Discussion

Discussion

The End

Thank you for coming!


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