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Proactive monitoring in natural environments

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Proactive monitoring in natural environments. Ian Marshall , Computing Laboratory, University of Kent [email protected] Technical Director of the Envisense Research centre http://envisense.org. Current research methods. Single expensive package In situ process studies - PowerPoint PPT Presentation
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Proactive monitoring in natural environments Ian Marshall, Computing Laboratory, University of Kent [email protected] Technical Director of the Envisense Research centre http://envisense.org
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Page 1: Proactive monitoring in natural environments

Proactive monitoring in natural environments

Ian Marshall, Computing Laboratory, University of [email protected]

Technical Director of the Envisense Research centre

http://envisense.org

Page 2: Proactive monitoring in natural environments

Current research methods• Single expensive

package• In situ process studies• Low spatial resolution• Short lifetime• Small areas

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Wireless Sensor networks

• Ad-hoc wireless communication• Physical measurement• No access to mains• Large area (sq kms)• Long life (months)• Many measurement points

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WSN management

• Low probability of manual intervention

• Highly dynamic, unpredictable environment

• Very unreliable nodes and comms• Need to automate response to

events• ‘model free’ adaptive control

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Peak district Experiments

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Floodnet

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SECOAS

Scroby sands wind farm and its impact on sedimentation processes

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CEFAS Survey

April 2002

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Mechanical General Arrangement

Buoy (yellow)

Radio equipment

Data cable Warp

Chain

Chain

WarpPlough anchor

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Real trial Oct-Nov 2004

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Initial Deployment Areas

1 NM

6 Sensors150m apart

Shore station

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Seabed Package

• Measure Oceanographic variables (15 minute cycle)

• Temperature (1 sample/min) • Pressure (1 sample/s for 5 mins)• Turbidity (10 samples/min)• Tilt (aka current) - (1 sample/s for 5 mins)• Conductivity (1 sample/min)

• Adapt sampling rates • Adaptively log data• Transmit selected data to radio buoy

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Adaptive sampling

• Measure, delete, combine, forward, sleep• Use local variability, neighbour variability and

internal state • Self configure using distributed evolutionary

“algorithm” (bacteria)• Can adjust priorities and frequency of actions• Can form groups (quorum sensing)• Reward set by user using a diffusion (gossip)

protocol – changes drive auto-reconfiguration of genome

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QoS on a Sensor Network

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Processing

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Summary

• Autonomous adaptive control is needed in environmental sensor networks

• Network protocols must support and respond to application semantics (be app aware)

• In simulation adaptation was almost as good as optimal sliding window

• In practice it dealt well with change from calm to stormy

• More research will be needed• www.secoas.org• www.envisense.org


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