2. KANTeNET Knowledge Enabled Sensor Network Middleware

3. Overview 1.Application Scenario 2.Case Studies 3.Middleware Architecture

4. Application Scenario Hit and Run

5. Application Scenario Witness places telephone call to police with description of suspect vehicle. Police place query to system with description of suspect vehicle.

6. Application Scenario Sensors scan environment for vehicles matching description. Several vehicles spotted and identified as possible matches.

7. Application Scenario Vehicle coordinates distributed to ground level sensors and Google Maps. License plate number and/or driver image captured and sent to DMV and watchlist databases.

8. Application Scenario Police patrol coordinates matched against suspect vehicle coordinates and interception orders distributed, along with: Continually updated Google map Info about vehicles Info about drivers

9. Application Scenario Result of Hit and Run

10. Application Scenario Stand off Staring Close in Staring Surface & Near-Surface Staring (SUAV, Bldg Sensors, Taggants, UGS) (“Cupid Fire”) ATR-Driven Small UAV on Steroids Sensor Aided Vigilance GWOT requires ability to operate seamlessly across layers to sense and track asymmetric threats. Puts increased demands on novel concepts for establishing and exploiting netted persistence and empirical phenomenal data. Key role for revolutionary taggant materials and advanced data management, all within an “integrated solutions” framework

11. SAVig Goal Continuously track dismounts and vehicles in complex urban environments. Objective Fingerprint, Detect, ID and Track Dismounts Vehicles Fortifications Ordinance / Weapons / IEDs Approach Hyperspectral Sensors and Imaging (HSI) Offer High Spatial and Spectral resolutions Understand and Exploit HSI phenomenology for detection and tracking of urban targets.

12. Architecture Recognition Detection Tracking Identification Recognition Detection Tracking Identification Recognition Detection Tracking Identification Storage Raw Data Specific Information Action Registration Collection Control Sensor Filter Registration Sensor Data Processing Hierarchy Data Management

13. Overview 1.Application Scenario 2.Case Studies 3.Middleware Architecture

14. Case Studies 1.GSN Global Sensor Network Digital Enterprise Research Institute (DERI) http://gsn.sourceforge.net/ 2.Hourglass An Infrastructure for Connecting Sensor Networks and Applications Harvard http://www.eecs.harvard.edu/~syrah/hourglass/ 3.IrisNet Internet-Scale Resource-Intensive Sensor Network Service Intel & Carnegie Mellon University http://www.intel-iris.net/ 4.SNSP Sensor Network Services Platform University of California, Berkley & DoCoMo http://chess.eecs.berkeley.edu/

15. GSN 1.Global Sensor Network 2.Sponsored by DERI 3.Open Source - http://gsn.sourceforge.net/ Conceptual Data Flow in a GSN Node GSN Container Architecture

16. Overview 1.Application Scenario 2.Case Studies 3.Middleware Architecture

17. Architecture Gateway Node Acquisition Annotation Integration Sensor Data Manager Live Data/Metadata Stream Archive Repository (Data/Metadata) External Integration (sensor web, web services, etc) Interface Manager / API Gateway Node Semantic Model Producer Layer Operation Layer Consumer Layer Query Manager Context Aware Repository

18. Semantic Enhancement Data Provenance Suppose a sensor network detects a shark swimming in the ocean. How can we verify that the identified object is a shark? We must look at the data used in the identification. Identification is a complicated process where the data can be continuously altered. Tracking data through such a workflow is notoriously difficult. Our solution is to annotate the sensor data throughout its life-cycle, from acquisition to response, so that transformations and analysis can be processed without losing contact with valuable intermediary data.

19. Semantic Enhancement Contextual Query Again, suppose a sensor network detects an object in the ocean, but cannot determine whether the object is a shark or a submarine? If we can access knowledge about the surrounding environment, then may be able to determine that the particular coordinates represent a known shark infestation. Now we may be able to reasonably make a determination using knowledge external to the sensor network. Our solution is to provide a suite of domain aware ontologies containing knowledge of not only the sensor network but also the deployed environment.