Transcript Acoustic localization for real-life wireless embedded sensor network

Acoustic localization
for real-life
wireless sensor network
applications
Michael Allen
Cogent Computing ARC
in collaboration with:
Centre for Embedded Networked Sensing, UCLA
WaveScope project, CSAIL, MIT
Wireless networked sensing
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Wirelessly networked, embedded, battery powered,
sensor enabled computers
Sample and process data about a physical phenomena
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Temperature, light, sound, image
Aims/advantages
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Cheap, pervasive, collaborative
Distributed computation
My Research
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Physical phenomena is sound - Acoustic localization:
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Real-life aspect
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For self/node-localization (locate nodes using acoustics)
For source localization (locate acoustic event of interest)
Real problems/questions, real environments
Systems research (reliability, robust behaviour)
Field-usable tools
Theoretical aspect
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Design principles, algorithms
Scalability
Data fusion
Motivating applications
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Primary motivation: bioacoustics
Acoustic source localization of animals/bird calls
 Position estimation is helpful for behaviour analysis
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Problems
Exploratory systems development is often required
 Currently available platforms are not suited to this
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Current work - VoxNet
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An Interactive platform for bioacoustics research
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Forms real-life, systems aspect of thesis research
Allow on-line and off-line operation
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Hardware and software
React to events in-field
Full data set gathered at node
Network consists of x nodes and 1 sink
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Sink is endpoint for programs
Nodes talk over multi-hop IP to sink
Sink/control
Hardware – Acoustic ENSBox
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More capable than current WSN research platforms:
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Rapidly deployable:
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32-bit ARM CPU, 64MB RAM
Four channel 48KHz audio
wi-fi/802.11b
internal battery (5-10hr)
Attended, short-lived deployments
Self-localization and time synchronisation:
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V2 (2007)
cm accuracy acoustic based localization (up to 100m range)
10us time synchronisation across network
L. Girod, M. Lukac, V. Trifa, and D. Estrin. "The Design and Implementation of a Self-calibrating Acoustic Sensing Platform." in Proc. of SenSys 2006
Deployment in Colorado
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Acoustic localization application running on platform
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Nodes run adaptive event detectors
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In-situ, on-line operation (detecting marmots)
Signal energy in frequency bands of interest
On detection, data is passed to sink (4 channels/node)
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Sink clusters together related events
Makes DoA estimates based on each node’s detection
Estimates position from crossing of DoAs
Allen, M., Girod, L., Newton, R., Madden, S., Blumstein, D., Estrin, D., “VoxNet: An Interactive, Rapidly-Deployable Acoustic Monitoring Platform”,
International Conference on Information Processing in Sensor Networks (IPSN 2008)
Problems/Observations
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Latency problems
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Uncoordinated, interfering network traffic
Event grouping at sink
Grouped by arrival time – BAD
 Events arrive out of order, late
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Overall position estimate took far too long
Link quality
 Multi-hop data transfer latency
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Improvements
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On-line clustering algorithm
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Group events based on detection time
Smart event grouping
Nodes only send notification of detection
 Sink requests data
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Adaptive behaviour trade-off
Nodes monitor network links
 Decide to process locally or pass raw data
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Future work
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Scalability of acoustic localization networks
Coverage, density – they make sense?
 Bounds on performance
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Data fusion for position estimate
Quickest way to get data and fuse it
 Information theory/Bayesian approaches to data
fusion
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