Sensor Network Embedded Intelligence

A Al-Anbuky, H Sabat, M I Rawi & S SivakumarSeNSe Research Centrehttp://SenSe.aut.ac.nz

AUT University, Auckland

Presentation OverviewInfo on the upcoming Co-Located ICT

conferences 2010SeNSe lab research overviewHuman Comfort and passive house researchWildlife Sensor network and network

connectivity researchData stream mining research

http://APCC2010.aut.ac.nz

2010 Co-located ICT conferences31 Oct – 3 Nov Auckland NZ

http://APCC2010.aut.ac.nz

2010 Co-located ICT conferences31 Oct – 3 Nov Auckland NZ

Empowering global connectivityToday we are confronted by global challenges such as climate change, resource consumption, environmental stress and population health. In responding to these challenges engineers are recognising the increasing importance of communications and connectivity. Sensor networks provide unprecedented volumes of information about our environments. Wireless and fixed communications networks facilitate the sharing of this information. Intelligence and cognition enable the efficient use and management of our resources. Meanwhile, humans and devices demand increasing communications connectivity and systems interoperability. Under the theme of "Empowering global connectivity", APCC 2010 provides a forum for researchers and engineers in the Asia-Pacific region to present and discuss topics related to advances in information and communication technologies, while encouraging collaboration and innovation that may help in saving the planet.

Partnership & Fund Raising

Available opportunities varies from $2.5k to $20k

Sponsors privileges could include Seats for membership within organizing

committee (Key sponsors only)Free seats for conference Registration

(sponsorship dependent) Logos on CFP, Conference web site and

proceedingListed as sponsor within the proceedings

The Venue

The Venue

SeNSe Lab -AUT

Wildlife Cognitive Sensor Network Mobile subjects localization Connectivity & opportunistic networks Wildfire hazard detection Hunters friendly fire avoidance Data stream mining & network energy efficiency

Object Centric Ambient Intelligence Human comfort & passive home ambient intelligence Thermal mapping & food property dynamic tracking pH sensor network & red meat tenderization

Vehicular Communication Train localization & railway signalling system

Microwave Sensing Timber property mapping

Distributed Signature Analysis Power System fault detection

Passive House Sensor NetworksMohD Izani Rawi

SeNSe LabAUT University

Overview

Passive House System OverviewArchitecture OverviewPassive House System ManagerThermal Comfort

Human Centric Thermal Comfort ConceptThermal Comfort OperationThermal Comfort SimulationThermal Comfort Result

Discussion & Further Work

Passive House System Overview

Architecture Overview

Human Centric Activity – Automation, personalisation, adaptation

Going homeMobile Device (ID)

Sensors / Actuators(Location, appliances, environmental)

•Going home•Mobile Device Notify Home•Personalise home environment•Learn occupant behaviour•Adaptation & personalisation

Passive House System Overview

Passive House System Manager

Human Comfort

ThermalComfort

VisualComfort

Indoor Air

Comfort

Acoustical

Comfort

SpatialComfort

PMV Light AQ Noise

OccupantPreferenc

es

ThermalVisualAir

EnergyUsage

PH Manager

ActuatorControl

Heating / CoolingWindow PositionShading PositionIlluminance Level

Heating / CoolingHot WaterAppliancesVentilation

Ta, MRT, RH, VelClo, Met

Illuminance LevelShading Level

CO2 Concentration Sound Level

Passive House System OverviewThermal Comfort

PMV Value Meaning+3 Hot+2 Warm+1 Slight Warm0 Neutral-1 Slight Cool-2 Cool-3 Cold

M: metabolismW: external work, equal to zero for most activityIcl: thermal resistance of clothingfcl: ratio of body’s surface area when fully clothed to body’s surface area when nudeta: air temperaturetr: mean radiant temperatureVa: air velocityPa: partial water vapour pressurehc: convectional heat transfer coefficienttcl: surface temperature of clothing

Human Centric Thermal Comfort Concept

Thermal Comfort OperationSingle Node PMV CalculationsTested on Sun SPOT wireless platformSeNSe lab air temperature & PMV

Human Centric Thermal Comfort ConceptThermal Comfort Simulation

PMV of a Given Living SpaceInverse Distance Weighted (IDW) interpolation

technique

Human Centric Thermal Comfort ConceptThermal Comfort Results

Thermal Comfort Parameters

Nodes

N1 N2 N3 N4 N0

DBT 24 23 21

22 22.50

MRT 27 25 20

21 23.41

RH 50 57 60

54 55.94

Vel 0.1 0.1 0.2

0.2 0.14

Met - - - - 1

Clo - - - - 1

PMV at N0 = -0.16

Energy Efficient Network Connectivity: Wildlife and Sensor Network

Sivakumar SivaramakrishnanSeNSe Lab

AUT University

Connectivity Issues in Wildlife Monitoring

Short Range Nodes

Network is Adhoc

Network Holes (region of no connectivity)

Movement results in Temporary Connectivity

Node Discovery

Varying Node DensityAnimals have different habitatThis determines the grouping of the nodes

Varying node Density Depending on Animal Habitat

Due to connectivity holes data transmission is opportunistic.

B

• Opportunistic Networking• Data Hand-off Mechanism• Adaptive Node Discovery• Doppler Shift to Detect

Direction

Hand-Off under Random motion of the animal

Energy Dissipation for Connectivity with and without Hand-off

Adaptive Opportunistic Connectivity

A

DFC

E

Due to Predictive Sampling: Fig. shows adaptive sampling saves on energy as the number of unsuccessful searches are less

Preliminary Results

Due to Hand-off: Fig. shows the energy consumption due to Hand-off scheme is less than without hand-off

Distributed Data Stream Mining in WSN Environment:

Efficient Fuzzy based Approach

Hakilo SabitSeNSe Lab

AUT University

Sensors data streamsA data stream can be roughly

thought as an ordered sequence of items, where the input arrives more or less continuously as time progresses.

Examples of data streams include computer network traffic, phone conversation, Web searches, Sensor data and etc.

Data streams are characterised by continuous flow of data with infinite length.

Sensors deployed for monitoring application (ex. traffic flow monitoring, environmental monitoring, patient health monitoring) produce data with such (data stream) characteristics.

Data steams generate large quantity of real-time/near real-time data (structured records).

The stream processing has to be done in real-time or near real-time and in bounded storage.

Data stream processing

WSN stream miningWSN are know for their limited resources (storage, processing

and energy).High resolution sensor data streams contain useful

information excellent environment for data mining

Fuzzy logic based distributed stream clustering algorithm (SUBFCM)

designed and optimised for WSN environment

The SUBFCM algorithm SUBFCM compute local clusters at

designated GH nodes and only transmit the local representatives- Reduced data bits to transmit means energy saving, besides bandwidth efficiency.

Based on single scan of data items to extract the representative patterns & no intermediate data stored - memory scalable.

• SUBFCM compute the complete cluster at a central location based on the local representatives•Stream modelling results will generate a control signal for the local nodes to adjust their parameters

Internet

Fire Department

Local Industry

Residents Data processing

centre

Sink

Sensor

Group head

0 20 40 60 80 100 1200

0.2

0.4

0.6

0.8

1

1.2

1.4x 10

-3

Total Distance [m]

Tota

l E

nerg

y [

Joule

s]

fcm, multi-hop fcm and subfcm algorithms

subfcm-->

-->fcm

multi-hop-->->q=70

24 25 26 27 28 29 3055

60

65

70

75

80

85

90

Temperature [oC]

Rela

tive H

um

idity [

%]

hot spot 1

hot spot 2

hot spot 3

0

5

10

15

20

25

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

Erro

r

No. of runs

Temp Erorr Average Temp Error

RH Erorr Average RH Error

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Erro

r

No. of runs

Temp Error Average Temp Error

RH Error Average RH Error

Energy consumption

Data reduction

Cluster accuracy vs central algorithm

Cluster accuracy vs fcm algorithm

Preliminary Results

Happy to Talkhttp://SeNSe.aut.ac.nz