Sensor Network Embedded Intelligence

1. Sensor Network Embedded Intelligence A Al-Anbuky, H Sabat, M I Rawi & S Sivakumar SeNSe Research Centre http://SenSe.aut.ac.nz AUT University, Auckland

2. Presentation Overview • Info on the upcoming Co-Located ICT conferences 2010 • SeNSe lab research overview • Human Comfort and passive house research • Wildlife Sensor network and network connectivity research • Data stream mining research

3. 2010 Co-located ICT conferences31 Oct – 3 Nov Auckland NZ http://APCC2010.aut.ac.nz

4. 2010 Co-located ICT conferences31 Oct – 3 Nov Auckland NZ http://APCC2010.aut.ac.nz

5. Empowering global connectivity Today 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 networksprovide 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.

6. 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 proceeding • Listed as sponsor within the proceedings

7. The Venue

8. The Venue

9. 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

10. Passive House Sensor Networks MohDIzaniRawi SeNSe Lab AUT University

11. Overview • Passive House System Overview • Architecture Overview • Passive House System Manager • Thermal Comfort • Human Centric Thermal Comfort Concept • Thermal Comfort Operation • Thermal Comfort Simulation • Thermal Comfort Result • Discussion & Further Work

12. Passive House System Overview Architecture Overview Sensors / Actuators (Location, appliances, environmental) • Going home • Mobile Device Notify Home • Personalise home environment • Learn occupant behaviour • Adaptation & personalisation Going home Mobile Device (ID) Human Centric Activity – Automation, personalisation, adaptation

13. Passive House System Overview • Passive House System Manager Actuator Control Heating / Cooling Window Position Shading Position Illuminance Level PH Manager Thermal Visual Air Occupant Preferences Energy Usage Heating / Cooling Hot Water Appliances Ventilation Human Comfort Light AQ Noise PMV Thermal Comfort Visual Comfort Indoor Air Comfort Acoustical Comfort Spatial Comfort Ta, MRT, RH, Vel Clo, Met Illuminance Level Shading Level CO2 Concentration Sound Level

14. Passive House System Overview Thermal Comfort M: metabolism W: external work, equal to zero for most activity Icl: thermal resistance of clothing fcl: ratio of body’s surface area when fully clothed to body’s surface area when nude ta: air temperature tr: mean radiant temperature Va: air velocity Pa: partial water vapour pressure hc: convectional heat transfer coefficient tcl: surface temperature of clothing

15. Human Centric Thermal Comfort Concept • Thermal Comfort Operation • Single Node PMV Calculations • Tested on Sun SPOT wireless platform • SeNSe lab air temperature & PMV

16. Human Centric Thermal Comfort Concept • Thermal Comfort Simulation • PMV of a Given Living Space • Inverse Distance Weighted (IDW) interpolation technique

17. Human Centric Thermal Comfort Concept • Thermal Comfort Results

18. Energy Efficient Network Connectivity: Wildlife and Sensor Network SivakumarSivaramakrishnan SeNSe Lab AUT University

19. Connectivity Issues in Wildlife Monitoring • Short Range Nodes • Network is Adhoc • Network Holes(region of no connectivity) • Movement results in Temporary Connectivity • Node Discovery

20. Varying Node Density • Animals have different habitat • This determines the grouping of the nodes Varying node Density Depending on Animal Habitat Due to connectivity holes data transmission is opportunistic.

21. Adaptive Opportunistic Connectivity • Opportunistic Networking • Data Hand-off Mechanism • Adaptive Node Discovery • Doppler Shift to Detect Direction B A E C F D Energy Dissipation for Connectivity with and without Hand-off Hand-Off under Random motion of the animal

22. Preliminary Results Due to Predictive Sampling: Fig. shows adaptive sampling saves on energy as the number of unsuccessful searches are less Due to Hand-off: Fig. shows the energy consumption due to Hand-off scheme is less than without hand-off

23. Distributed Data Stream Mining in WSN Environment: Efficient Fuzzy based Approach HakiloSabit SeNSe Lab AUT University

24. Sensors data streams • A 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.

25. Data stream processing • 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.

26. WSN stream mining • WSN 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

27. 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

28. Preliminary Results Data reduction Energy consumption Cluster accuracy vs central algorithm Cluster accuracy vsfcm algorithm

29. Happy to Talk http://SeNSe.aut.ac.nz