a reputation and trust based multi- modal sensor network...
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Beaufort Research Awards in Marine Sensing
A Reputation and Trust Based Multi-
Modal Sensor Network for
Environmental Monitoring
Edel O’Connor
Prof. Alan F. Smeaton & Prof. Noel E. O’Connor
This Beaufort Marine Research Award is carried out under the Sea Change
Strategy and the Strategy for Science Technology and Innovation (2006-
2013), with the support of the Marine Institute, funded under the Marine
Research Sub-Programme of the National Development Plan 2007–2013.
Presentation Outline
Issues with in-situ WSNs.
Multi-modal sensor networks.
Data aggregation.
Pilot studies:– River Lee water depth study.
– Water level prediction for adaptive sampling.
Trust and reputation framework.
Beaufort Research Awards in Marine Sensing
Water Management
• Water management is an
important part of the monitoring
of the natural environment.
• For many years water managers
relied on field measurements for
coastal monitoring and water
quality evaluation.
• However this process is being
revolutionised through the
introduction of new technologies
such as sensor networks.
Beaufort Research Awards in Marine Sensing
Image: John Cleary
Issues
• Current state of the art in chemo/bio-
sensor networks not at a stage for
reliable long-term large scale deployment.
• Even without the complexity of chemo-bio
sensing, still considerable issues
– Sensors subject to harsh conditions
– Bio-fouling
– Limited spatial resolution
– Difficult to monitor large areas over long
periods of time
– Unsuitable for certain environments and
the immediate detection of certain events
– Developments in sensor research pushing
towards ever cheaper systems
– Huge information overload – user
requires reliable event detection. Image:
www.ferrybox.eu/imperia/md
/images/ferryboxuse
Multi-modal sensor networks
• The incorporation of alternative sensing modalities
such as visual sensors, alongside an in-situ WSN can
help to overcome some of these problems.
Beaufort Research Awards in Marine Sensing
Test Sites
Requirements River Lee Galway Bay River Tolka
Network x x
Power x x
Security x x
Multiple
sensing
modalities
x x
Interesting from
marine
perspective
x x x
Data Aggregation – Camera data
Beaufort Research Awards in Marine Sensing
Data Aggregation – Satellite data
Beaufort Research Awards in Marine Sensing
In-situ sensor data and context data
Beaufort Research Awards in Marine Sensing
Deploy: River Lee
SmartBay: Galway Bay
Image: www.deploy.ie (IDS: Intelligent Data Systems)
Rainfall Radar processing
Image: Marine Institute
River Lee Water Depth Study
-2
0
2
4
6
8
10
12
14
00
:10
:43
02
:36
:39
05
:02
:34
07
:28
:29
09
:54
:24
12
:20
:19
15
:00
:50
17
:26
:45
19
:52
:40
22
:18
:35
00
:44
:30
03
:10
:25
05
:36
:21
08
:02
:15
10
:28
:11
12
:54
:06
15
:20
:01
17
:45
:56
20
:11
:51
22
:37
:46
01
:03
:41
03
:29
:36
05
:55
:32
08
:21
:27
10
:47
:23
13
:13
:18
15
:39
:12
18
:05
:07
20
:31
:02
22
:56
:57
Fe
et
Time
Water Depth 18-20 July 2008
-2
0
2
4
6
8
10
12
00
:02
:07
00
:45
:53
01
:29
:40
02
:13
:27
02
:57
:13
03
:41
:00
04
:24
:46
05
:08
:33
05
:52
:19
06
:36
:06
07
:19
:52
08
:03
:39
08
:47
:26
09
:31
:12
10
:14
:59
10
:58
:45
11
:42
:32
12
:26
:18
13
:10
:05
13
:53
:51
14
:52
:14
15
:36
:00
16
:19
:47
17
:03
:33
17
:47
:20
18
:31
:06
19
:14
:53
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:58
:39
20
:42
:26
21
:26
:12
22
:09
:59
22
:53
:45
23
:37
:32
Fe
et
Time
Water Depth 03 July 2008
-5
0
5
10
15
20
25
30
35
00
:14
:32
00
:58
:18
01
:42
:04
02
:25
:51
03
:09
:37
03
:53
:24
04
:37
:10
05
:20
:57
06
:04
:43
06
:48
:30
07
:32
:17
08
:16
:03
08
:59
:50
09
:43
:36
10
:27
:23
11
:11
:09
11
:54
:56
12
:38
:42
13
:22
:29
14
:06
:15
14
:50
:02
15
:33
:48
16
:17
:35
17
:01
:21
17
:45
:08
18
:28
:54
19
:12
:41
19
:56
:27
20
:40
:14
21
:24
:00
22
:22
:22
23
:06
:09
23
:49
:55
Fe
et
Time
Water Depth 22 July 2008
C1 C2 C3
Class Distance Error
0.642 0.537 0.302
Classification Rate
0.467 0.732 0.750
Multi-modal sensor networks – adaptive
sampling
Beaufort Research Awards in Marine Sensing
Reputation and Trust-based multi-modal sensor
network Development of a reputation and trust-based multi-modal sensor
network
Adaptation of a model developed for in – situ sensor networks known as
RFSN (RFSN Ganeriwal & Srivistava 2008).
Adapation of this model to multi-modal sensor networks
Beaufort Research Awards in Marine Sensing
Community of Sensor Nodes
Node
j
Node
k
Node
l
Node
m
Node
i
Node
j
Node
k
Node
l
Node
m
Node
i
RFSN
RFSN
RFSN
RFSN
RF
SN
RF
S
N
RF
S
N
RF
S
N
Community of Sensor Nodes
RFSN
[j,k,l,m]
WATCHDOG
Series of outlier detection protocols, outputs cooperation metrics for each of the nodes.
Cooperation
metrics
REPUTATION
Updates reputation for each of the nodes [I, j, k,l] i.e. Rij, Rik, Ril, Rim
trust
External evidence
Community of Sensor Nodes
Node
j
RF
SN
Community of Sensor Nodes
RFSN
[j,k,l,m]
WATCHDOG
Series of outlier detection protocols, outputs cooperation metrics for each of the nodes.
Cooperation
metrics
REPUTATION
Updates reputation for each of the nodes [I, j, k,l] i.e. Rij, Rik, Ril, Rim
trust
External evidence
To sum up………..
• Multi-modal sensor networks provide:
– Increased information and early warning
information regarding environmental events.
– More efficient and effective sensing.
– More reliable event detection which leads to
improved monitoring and scientific analysis.
– A smarter adaptive sensor network that
continuously monitors our environment, detects
changes in the quality of our environment and
reacts to those changes.
Beaufort Research Awards in Marine Sensing
Acknowledgements• Beaufort Research Awards in Marine Sensing
• Marine Institute
• Prof. Alan Smeaton, Prof. Noel O’Connor, Prof. Dermot
Diamond.
• CLARITY – Centre for Sensor Web Technologies, Science
Foundation Ireland under grant 07/CE/I1147
• SmartBay project
• Deploy project
• HRDDS (GHRSST Project) and David Poulter at the National
Oceanography Centre, Southampton, UK
Beaufort Research Awards in Marine Sensing
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