MESSAGE: A Mobile Environmental Sensing
System Across a Grid Environment
Jeremy Cohen
Department
p
of Computing
p
g
Imperial College London
OGSA-DAI Data Integration Meeting
NeSC, Edinburgh
16th March 2009
The MESSAGE Project
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• 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 demonstration 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 Industrial Partners
Equipment providers
Service providers
End users
MESSAGE Objectives
j
„
To extend existing eScience, sensor, communication and modelling technologies
to enable the integration of data from heterogeneous fixed and mobile
environmental sensors 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
p
y dynamic
y
data on,, for example,
p , weather conditions,,
transport network performance, vehicle mix and performance, driver behaviour,
travel demand, pollutant exposure and health outcomes.
„
To implement relevant eScience tool sets and (fixed and mobile) sensor and
communication system 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.
Technical Scenarios – Pollution Data
Traffic Management
Examining how increased environmental data
can be used for better network control
Key issues to be addressed...
y Inclusion of environmental objectives
j
in Traffic Control
y Resolution - reliability – cost tradeoffs
y Distributed processing of air quality projections
Traveller Information
Disseminating
g environmental data directly
y 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 – Pollution Data
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
Real-time,
time, archive and predicted data analysis
y Resolution - reliability – cost tradeoffs
y Dynamic sensor resolutions
e-Science Platform – Data Capture
Data Capture Platform
Reliable, efficient capture of data from an environment with an unreliable
communications infrastructure and varying load
load.
y Different types of sensors, different pre-processing requirements
y Different communications technologies
y Real
R l titime streaming
t
i and
d iintermittent
t
itt t b
burstt
Scalable Cloud-based
processing infrastructure
Multiple sensor and
communications technologies.
Multiple DBs
distributed across
several sites.
sites
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
XML Data Transport
p 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>
g
g
<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>
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>
Data Formats - UTMC Schema
Source: Visalakshmi Suresh, MESSAGE Project, North East
Regional e-Science Centre
e-Science Platform – Data Capture
Th
Three
Layer
L
Architecture
A hit t
• Application Layer
• Realtime Data Layer
• Sensor Layer
e-Science Platform – Analysis
Data Analysis
Real time data analysis requires the balancing
of a number of factors to identify the available
capacity for carrying out real time data analysis:
y Algorithms
y Local/Remote processing power
y Data volumes
y Communications bandwidth
y Cost
Visualisation
Making live/historic data available for analysis
by individuals (e.g. transport planners,
scientists,
i ti t etc.)
t )
• Many visualisation platforms/APIs available
• Real time integration with third party data
e-Science Platform – Analysis
In-network Analysis
Where computational power and communications allow,
in-network processing and analysis of data presents
many opportunities. MESSAGE is prototyping a system to
demonstrate this:
• Peer-to-Peer (P2P) data sharing between local devices
• Use of K-means clustering algorithm
between a group of nodes
• Potential for hotspot identification,
local dispersion prediction, …
• Event triggers sent to central platform
platform.
Local Processing Platform
ARM-based processing nodes running Linux. 802.11
WiFi (ad-hoc), Bluetooth, GPS, USB connectivity.
Varietyy of software and interfacing
g options.
p
Field Trials – South Kensington
DUVAS sensors Route A
DUVAS sensors Route B
DUVAS sensors – static
Newcastle MOTEs – static
Cambridge sensors –
cyclists/pedestrians
Q /
/
coAQM/DUVAS/MOTE
located deployment
Field Trials
Early testing is underway
Earl
nder a for a co
co-ordinated
ordinated
field trial that will be carried out in South
Kensington to demonstrate the capture,
streaming and processing of pollution data from
different sensor types into a co-ordinated
platform for analysis and visualisation.
OGSA-DAI/DQP in MESSAGE
End-user/application queries
OGSA-DQP
OGSA-DAI
OGSA-DAI
OGSA-DAI
DB
DB
DB
London
Newcastle
Cambridge
Data from
sensors
Data from
sensors
Data from
sensors
THANK YOU!
jeremy.cohen@imperial.ac.uk
robin north@imperial ac uk
robin.north@imperial.ac.uk
www.message-project.org
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