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Data Dissemination for Environment Monitoring

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Title: Data Dissemination for Environment Monitoring


1
Data Dissemination for Environment Monitoring
Visalakshmi Suresh Prof. Paul Watson
2
MESSAGE
  • EPSRC funded research.
  • Consortium
  • Newcastle University, Leeds University, Imperial
    College, Cambridge and Southampton University
  • Advisory Board Collaboration with Industry

3
Overview
  • Novel and Emerging ITS sensing devices
  • Data acquisition and Processing
  • Real Time Data Fusion Architecture
  • Real Time Data Dissemination
  • Historic Data Mining using Collaborative Tools
  • Conclusion

4
Novel and Emerging ITS sensing devicesPervasive
Sensors
  • Highly modular, mobile/fixed
  • CO, NO2, Noise and Traffic
  • Temperature and Humidity
  • Accelerometer and GPS
  • Mobile and Fixed
  • Battery 6months _at_1min average
  • 200 components cost
  • Motes 100m apart
  • Zigbee wireless 5 hops
  • Gateway Indoor/outdoor.
  • LAN or autonomous (GPRS)

5
Novel and Emerging ITS sensing devicesLegacy
Systems
A
  • Technology
  • Vehicles
  • People
  • Systems

6
MESSAGE Decision Support System
Network Status
Modelling
Evaluation and Assessment
Disseminate/inform
7
Real Time Mashups
Locations
Real Time Monitoring
Data Aggregation
Pollution Hotspots
AURN -- Legacy System
8
Demonstration
  • Real Time Dissemination

9
Real Time and Historic Integration
OSPM Dispersion Model
Technology
10
Traffic and air quality Model Validation
A
11
Traffic and air quality Model Validation
A
12
Current Research
  • Integrating Models with Monitored data
  • (Image Source TORG, Newcastle
    University)

13
Data Display Marts
  • V Visualisation
  • RTEP Real-Time Event Processor
  • CCLAV Critical Congested Link Assessment
  • CA Congestion Assessments
  • TTEE Traffic Tail-pipe Emissions Estimation
  • RPPL Receptor Point Pollution Level

14
Collaborative Workbench Historic Data analysis
  • eScience Central
  • Store
  • Analyse
  • Automate
  • Share

15
System ArchitectureLayered Approach
16
  • Summary
  • Designed for data intensive applications
  • Features for continuous evolution
  • Integration of heterogeneous data sources
  • Effective control and added values to ITS
    implementation

17
Questions?
Visalakshmi Suresh Visalakshmi.suresh_at_ncl.ac.uk
Prof. Paul Watson paul.watson_at_ncl.ac.uk
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