The MESSAGE Project:
Mobile Environmental Sensing System Across a
Grid Environment
Jeremy Cohen
Imperial College London
EPSRC/TSB Pilot Projects Meeting
NeSC, Edinburgh
17th July 2008
The MESSAGE Project
• Mobile Environmental Sensing System Across a Grid Environment
• 3 year project starting October 2006
• Funded jointly by EPSRC and DfT (~£4m), under
EPSRC’s e-Science programme
• 5 Universities, 19 industrial partners
• Pioneering combination and extension of leading
edge grid, sensor, communication and positioning
technologies
• Create radically new sensing infrastructure based
on combination of ad-hoc mobile and fixed sensors
• www.message-project.org
MESSAGE Project Partners
MESSAGE Objectives
„
To extend existing e-Science, sensor, communication and modelling
technologies to enable the integration of data from heterogeneous fixed and
mobile environmental sensor grids in real time to provide dynamic estimates
of pollutant and hazard concentrations.
„
To demonstrate how these data can be usefully correlated with a wide range
of other complementary dynamic data on, for example, weather conditions,
transport network performance, vehicle mix and performance, driver behaviour,
travel demand, pollutant exposure and health outcomes.
„
To implement relevant e-Science tool sets and (fixed and mobile) sensor and
communication systems in a number of selected real-world case study
applications, involving close collaboration with business and the public sector,
and to thereby to demonstrate their value to the research and policy community.
Key research challenges
• Required data resolution and quality?
• Sensors
• Positioning
• Modelling
• System scalability?
• Implementation costs
• Operational costs
• Integrated data mining?
• Integration of models and measurements
• Distributed storage and processing of data
• Integration with many data providers
Technical Scenarios
Traffic Management
Examining how increased environmental data
can be used for better network control
Key issues to be addressed...
y Inclusion of environmental objectives in Traffic Control
Resolution - reliability – cost tradeoffs
y Distributed processing of air quality projections
Traveller Information
Disseminating environmental data directly to
individual travellers
Key issues to be addressed...
y Data aggregation across wide spatial areas
y Visualisation of temporally variant data
y Interfacing systems with legacy applications
Technical Scenarios
Health and Exposure
Monitoring and reporting potential or actual
exposure of individuals to pollutants
Key issues to be addressed...
y High spatial resolution monitoring
y Distributed data buffering and processing
y Path based data aggregation
Pollution Modelling
Using finer environmental data as inputs to
increasingly detailed pollution models
Key issues to be addressed...
y Real-time, archive and predicted data analysis
Resolution - reliability – cost tradeoffs
y Dynamic sensor resolutions
MESSAGE – Research Areas
„ Field Units
„ Sensors, Positioning, Communications
„ e-Science
„ Scalability, Distributed data mining
„ Online estimation of pollutant hotspots
„ Management of transport and environment
„ Provision of relevant traveller information
„ Environmental parameters in traffic control
MESSAGE – Field Units
3 field unit types
(Sensor + Pos + Comms)
Newcastle
• Electrochemical + GPS + Zigbee
Cambridge
• Electrochemical + RSSI + Cellular
MESSAGE – Field Units
Imperial
• GUSTO / Electrochemical
+ wireless ranging + WiFi
E-Science Architecture
e-Science Hardware
Architecture Overview
Three Layer Architecture
• Application Layer
• Realtime Data Layer
• Sensor Layer
Architecture: Sensor Layer
XML Data Transport Format
Common Data
transmission format
Investigating other
forms of XML
serialisation for
increased efficiency
<?xml version="1.0" encoding="UTF-8"?>
<sd:DataPacket xmlns:sd="http://www.message-project.org/xml/2007/03/sensordata" >
<sensorID>d8091be8-1513-4b89-9f9e-8391ba34bd10</sensorID>
<packetOrder>-1</packetOrder>
<timestamp>2007-05-22+01:00</timestamp>
<location>
<latitude>51.51526</latitude>
<longitude>-0.14200687</longitude>
<height>-1.0</height>
<bearing>-1.0</bearing>
<speed>-1.0</speed>
<quality>0.0</quality>
</location>
<pollutionData>
<CO>23.0</CO>
<PM10>0</PM10>
<PM2_5>0</PM2_5>
<PM1>0</PM1>
<PM0_1>0</PM0_1>
<NO>0</NO>
<NO2>0</NO2>
<NOx>0</NOx>
<O3>0</O3>
<SO2>0</SO2>
<CO2>0</CO2>
<noise>0</noise>
</pollutionData>
</sd:DataPacket>
Architecture: Realtime Data Layer
Data Formats - UTMC Schema
Data Formats - UTMC Schema
SystemCodeNumber
ShortDesc
Long Description
DS_TypeID
Unique identifier for the device
Short Description -- Short description of the device
Long Description -- Long description of the device
Data Source Type ID
Northing
Easting
NetworkPathRef
TransportLinkRef
TypeID
Link_Distance
Location of the device in OS grid coordinates
Creation_Date
Date/time at which the device was entered into the database
Deletion_Date
Quality_ID
Date of deletion
Reference to quality model
DS_TypeID
COMMONSUPP_TYPEID
DESCRIPTION
Identifier for equipment source like (ANPR, AQ, Sensors)
Reference to the TYPEID_SUPPORT table
Additional Notes
TypeID
TypeDesc
TypeNotes
Identifier for equipment type
Type Description -- Description of equipment type
Additional Notes
Network Path Reference -- Reference to Network Link
Transport Link Reference -- Reference to Transport Link
Type of equipment from generic list
Distance of the device from the start of the link
Data Formats - UTMC Schema
SystemCodeNumber
Last Updated
Period
Unique identifier for the device.
Time to which dynamic data relates
Period in minutes over which the data was collected
Quality Ref
Reference to quality of information for the dynamic data
Category Name
Pollutants category such as Co, NO, NO2, NOX, PM10, Relative
Humidity ..
Category Value
Measurements for the pollutant category.
POSTION_LAT
Geographic location of the sensor in terms of Latitude. Useful in
dynamic data
POSITION_LONG
Geographic location of the sensor in terms of Longitude. Useful in
dynamic data
SystemCodeNumber
Configuration Date
Unique identifier for the device.
Time at which configuration data was set
Category Name
Category Value
Pollutants category
Maximum value/ Threshold for each pollutant category
Architecture: Application Layer
Architecture: Application Layer
• Application Group specific Data Marts
• Data updated at longer intervals to avoid overloading realtime
data infrastructure.
• Data Marts use Star Schema design.
• Data pre-processed to suit specific application types
• Applications query data from Data Marts, access to real
time data possible but use restricted.
• Application Group Examples
• Air Quality Models
• Traffic Control Models (requires realitime + archived data)
• Health Care Monitoring (e.g. warnings to asthma sufferers)
Standards / Implementation
• XML schema for data transport - easy sharing of data
• External interfacing issues
• Making data available / Consuming external data
• Common frontend WS interface, backend implementation
open - (e.g JDBC interface, Hibernate, OGSA-DAI/DQP,
Oracle data processing)
• Java library for mapping internal data to Java objects
• Simplify interoperability
• Standard interfaces presented to external entities
• Plugin architecture - easily add new sensor specific formats
Scalable Compute Environment
Ongoing work
• Continue architecture implementation / testing
• Utility Computing Platform work
• Further machine image development and testing
• Machine Image optimisation - prototype complete
• Security
• Next generation field unit development
• Hardware platform modifications
• Software development
• Integration of transport and AQ models
• Dynamic deployment into Utility environments
Field Trials and Demo
London Field Trials
• Deployment from Q4 2008 in cooperation with TfL
• Mix of sensor types
• Newcastle and Imperial
• Mobile and static
• Varying parameters and resolution
• Interface to other data systems, weather, traffic
management etc
• Support analyses over different timescales and for
different objectives
London Field Trials
1. “Hotspot”
identification and
traffic management
2. Streetscape
design and
exposure to
pollutants
3. Security and
incident response
Victoria, London
Opportunities for innovation
• Links between modelling and measurement
• Adaptive observations
• Operational AQ models
• Design and deployment of efficient monitoring networks
• Citizen contributions (web 2.0) and pervasive
sensing/computing
• Experiments at London “supersite”
• Coordinated deployment of complementary sensing
• Simultaneous studies to benefit from common datasets
System simulation
• Online simulation of sensor
network behaviour
• Simulated sensor types,
following defined routes, with
simulated sensor values
• Root gateway, sensor gateway
and data service instances
• Dynamic update of layers and
database queries
• Visualisation using Google maps
THANK YOU!
jeremy.cohen@imperial.ac.uk
robin.north@imperial.ac.uk
www.message-project.org
www.imperial.ac.uk/cts
www.imperial.ac.uk/internetcentre