a reputation and trust based multi- modal sensor network...

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.


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.


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.


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


Data Aggregation – Satellite data


In-situ sensor data and context data


Deploy: River Lee

SmartBay: Galway Bay

Image: www.deploy.ie (IDS: Intelligent Data Systems)

Rainfall Radar processing

Image: Marine Institute

http://www.deploy.ie/

River Lee Water Depth Study

-2

0

2

4

6

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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

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:18

:35

00

:44

:30

03

:10

:25

05

:36

:21

08

:02

:15

10

:28

:11

12

:54

:06

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:20

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:45

:56

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:11

:51

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:37

:46

01

:03

:41

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:29

:36

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:55

:32

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:21

:27

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:47

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13

:13

:18

15

:39

:12

18

:05

:07

20

:31

:02

22

:56

:57

Fe

et

Time

Water Depth 18-20 July 2008

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Fe

et

Time

Water Depth 03 July 2008

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0

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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


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


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


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


Node

j

RF

SN


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.


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


a reputation and trust based multi- modal sensor network...

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