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Acoustic localization for real-life wireless sensor network applications

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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Acoustic localization for real-life wireless sensor network applications

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  1. 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

  2. Wireless networked sensing • Wirelessly networked, embedded, battery powered, sensor enabled computers • Sample and process data about a physical phenomena • Temperature, light, sound, image • Aims/advantages • Cheap, pervasive, collaborative • Distributed computation

  3. My Research • Physical phenomena is sound - Acoustic localization: • For self/node-localization (locate nodes using acoustics) • For source localization (locate acoustic event of interest) • Real-life aspect • Real problems/questions, real environments • Systems research (reliability, robust behaviour) • Field-usable tools • Theoretical aspect • Design principles, algorithms • Scalability • Data fusion

  4. Motivating applications • Primary motivation: bioacoustics • Acoustic source localization of animals/bird calls • Position estimation is helpful for behaviour analysis • Problems • Exploratory systems development is often required • Currently available platforms are not suited to this

  5. Current work - VoxNet • An Interactive platform for bioacoustics research • Hardware and software • Forms real-life, systems aspect of thesis research • Allow on-line and off-line operation • React to events in-field • Full data set gathered at node • Network consists of x nodes and 1 sink • Sink is endpoint for programs • Nodes talk over multi-hop IP to sink Sink/control

  6. V2 (2007) Hardware – Acoustic ENSBox • More capable than current WSN research platforms: • 32-bit ARM CPU, 64MB RAM • Four channel 48KHz audio • wi-fi/802.11b • internal battery (5-10hr) • Rapidly deployable: • Attended, short-lived deployments • Self-localization and time synchronisation: • 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

  7. Deployment in Colorado • Acoustic localization application running on platform • In-situ, on-line operation (detecting marmots) • Nodes run adaptive event detectors • Signal energy in frequency bands of interest • On detection, data is passed to sink (4 channels/node) • 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)

  8. Problems/Observations • Latency problems • Uncoordinated, interfering network traffic • Event grouping at sink • Grouped by arrival time – BAD • Events arrive out of order, late • Overall position estimate took far too long • Link quality • Multi-hop data transfer latency

  9. Improvements • On-line clustering algorithm • Group events based on detection time • Smart event grouping • Nodes only send notification of detection • Sink requests data • Adaptive behaviour trade-off • Nodes monitor network links • Decide to process locally or pass raw data

  10. Future work • Scalability of acoustic localization networks • Coverage, density – they make sense? • Bounds on performance • Data fusion for position estimate • Quickest way to get data and fuse it • Information theory/Bayesian approaches to data fusion

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