• Save
Dt Italk
Upcoming SlideShare
Loading in...5

Dt Italk



SECOAS publication, public download, permission request pending.

SECOAS publication, public download, permission request pending.



Total Views
Views on SlideShare
Embed Views



1 Embed 1

http://www.linkedin.com 1



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Dt Italk Dt Italk Presentation Transcript

    • Proactive monitoring in natural environments Ian Marshall , Computing Laboratory, University of Kent [email_address] Technical Director of the Envisense Research centre http://envisense.org
    • Current research methods
      • Single expensive package
      • In situ process studies
      • Low spatial resolution
      • Short lifetime
      • Small areas
    • Wireless Sensor networks
      • Ad-hoc wireless communication
      • Physical measurement
      • No access to mains
      • Large area (sq kms)
      • Long life (months)
      • Many measurement points
    • 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
    • Peak district Experiments
    • Floodnet
    • SECOAS Scroby sands wind farm and its impact on sedimentation processes
    • CEFAS Survey April 2002
    • Mechanical General Arrangement Buoy (yellow) Radio equipment Data cable Warp Chain Chain Warp Plough anchor
    • Real trial Oct-Nov 2004
    • Initial Deployment Areas 1 NM 6 Sensors 150m apart Shore station
    • 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
    • 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
    • QoS on a Sensor Network
    • Processing
    • 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