KANTeNET Knowledge Enabled Sensor Network Middleware

Overview

1. Application Scenario

2. Case Studies

3. Middleware Architecture

Application Scenario

Hit and Run

Application Scenario

Witness places telephone call to police with description of suspect vehicle.

Police place query to system with description of suspect vehicle.

Application Scenario

Sensors scan environment for vehicles matching description.

Several vehicles spotted and identified as possible matches.

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.

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

Application Scenario

Result of Hit and Run

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

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.

Architecture

Recognition

Detection

Tracking

Identification

Recognition

Detection

Tracking

Identification

Recognition

Detection

Tracking

Identification

Storage

Storage

Storage

Storage

Storage

Raw Data

Specific Information

Action Action Action

Registration

Collection

Control

Sensor

Filter

Registration

Sensor Data

Processing Hierarchy

Data Management

Overview

1. Application Scenario

2. Case Studies

3. Middleware Architecture

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/

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

Overview

1. Application Scenario

2. Case Studies

3. Middleware Architecture

Architecture

Gateway Node Gateway Node

Acquisition

Annotation

IntegrationSensor Data Manager

Live Data/Metadata Stream Archive Repository(Data/Metadata)

External Integration(sensor web, web services, etc)

Interface Manager / API

Gateway Node

SemanticModel

ProducerLayer

OperationLayer

ConsumerLayer

Query Manager Context Aware Repository

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.

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.