INFOD for Wide-Area Sensor Networks

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© 2006 Open Grid Forum INFOD for Wide-Area Sensor Networks OGF21, Seattle, WA, USA M. Shankar, ORNL, [email protected]

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INFOD for Wide-Area Sensor Networks. OGF21, Seattle, WA, USA. M. Shankar, ORNL, [email protected]. Overview. Wide-area sensor networks context Typical paradigms of communication Mapping a use-case to INFOD Processing alerts (and events) with INFOD - PowerPoint PPT Presentation

Transcript of INFOD for Wide-Area Sensor Networks

Page 1: INFOD for Wide-Area Sensor Networks

© 2006 Open Grid Forum

INFOD for Wide-Area Sensor Networks

OGF21, Seattle, WA, USA

M. Shankar, ORNL, [email protected]

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Overview

• Wide-area sensor networks context• Typical paradigms of communication• Mapping a use-case to INFOD

• Processing alerts (and events) with INFOD

• Potential gaps/mismatches to address in implementation and evaluation

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GIS Situational Awareness(ArcView or Google Earth, Browsers, …)

HPAC with Live Weather Feeds

8 chem/5 rad/5 video /1 weather sensors

Access control

WFS, OLS,…

Filter Agents

HTTPS: XML-RPC, SOAP

Replicated storage, image, video server

Fusion Center Portal and Viewer (Web Server; Database; GIS (Google); HPAC plume modeling)

Plotting of Data, Display Video Feeds

County Sheriff SensorNet Mobile System

Application Scenario: County Fusion Centers

Fielded Sensors

5 chem/ 1 weather sensors

NOAA Live Regional Weather

Subscriber

Publisher

Publisher

Distributed Wide-Area Middleware•Prototype and Analysis•Distributed querying and top-down programming

•Policy-based data-sharing•Asynchronous messaging

UT ORNL

Industry

INFO-D

1

2

3 4

Application infoEmergency updatesResponder data

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Paradigms of Communication

• Known Destinations (or Endpoints)• Point-to-Point (e.g., email)• Point-to-Multipoint (e.g., lists)• Subscribe for information and notify on availability (e.g.,

Priceline; OASIS WSN)

• “Sharing” – crucial for local, regional, federal; unknown recipients• Web-page; Bulletin Board• Chat-rooms (e.g., AOL/Yahoo/Google Chat); Topic based (e.g.,

Battelle DMIS)• Subscription from data provider (not known a priori) - INFOD

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Alerting Scenario Components

ABC Chemicals

Fire Station

Fire Station

Hospital

Police/ First Responders

County Office/E911/ Models

Publisher of Alerts

ConsumerConsumer of alerts and weather information. Also, Publisher. Identifies modes for consumers to be alerted based on different levels of toxicity

Consumer

Service Providers

Consumer

Consumer

Consumer

Consumer

Consumer

Weather Sensor

Weather (Poll/Pub)

ConsumerConsumer

Subscriber, defines subscription based on client necessities.

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SensorNet Data Dissemination

SensorNet Prototypes and ProjectsData-flow from edge to hierarchical datacenters –

accessed by application queries (SensorNet Node/ WFS/ ArcGIS/ Browsers/ Google Earth)

Alerting based on publish-subscribe using SAS/XMPP

Deployment Scenario Examples:• Weigh Station Monitors; Port of Entry Monitors• Area Situational Awareness and Threat detection

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Sample Use Case: Actors and the Message Flows

Message Exchange Information Exchange

Weather StationCoordination/Meta-Data: A Registry

First Responder 1

Publisher Publisher

First Responder 2

First Responder 3

APD 2000Chemical Sensors

Alerting System

Plume Analysis

Consumer

Consumer

Consumer

Subscriber/Consumer/Publisher

E911 Center

Subscriber

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Abstracting the Components in an Alerting Scenario

Applications

Node

Node

Sensors produce data and consume data from Node Applications Applications

Data-CenterApplicationsNode or Local

Data-Hub

Registry matches sensor data publishers and application subscribers (and consumers)

Publish-Subscribe Example Context: Flow of Data

INFO-D Registry

Registration and Notification of Consumer Presence

Subscription

Other entities may similarly subscribe and publish

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Event Results to be Communicated from Sources to Sinks

Publish-Subscribe paradigm works well here.• Subscriptions can equate to “first class objects” capturing state-

changes• Independent engine can inject subscriptions (i.e., independent entity can

make decisions for the system dynamically)• Meta-data (properties, subscriptions, etc.) stated in extensible

vocabularies enable formation of communities of interest• Publish from source based on a subscription

• Typically infer and create plumbing for the main connections• Create dynamic filtering mechanism for the actual data• Filtering and event correlations are often on trees that are one-level

deep. More complex trees/graphs could be broken down into constituents.

..

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Traditional System Behavior

Software/ Hardware Source

Event Stream: X

Temporary or Local Archival Storage

Event Stream: Y Event Stream: Z

Typical Request: if ((X and Y) or (X and !Z)) => InterestingEvent

Consumer + Subscriber

Publisher/Node/ Computation/ Processing

Data Center

Query to Data Center

Distributed/Federated Queries

Centralized Model

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INFOD Entities Behavior

Software/ Hardware Source

Event Stream: X

Temporary or Local Archival Storage

Event Stream: Y

Event Stream: Z

Originator and Sink

Publisher/Node/ Computation/ Processing

Interest: Query or Lookup

Compile + Mapping

Distributed Model

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INFOD Features Desirable for Wide-Area Sensor Networks

• Publishers and Subscribers can be unaware of each other but instead meet as a generalized vocabulary-based community of interest

• Both subscription and publication information is explicitly described• Generic Vocabulary Implementation• Extensible structure

• Matching performed with “expressions”• Data-constraints allow filtering of publisher’s

content

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Evaluation Dimensions: Applicability of INFOD

• Expressive power of subscriptions• Flexibility for dynamic behaviors• Vocabulary management• Security• Performance• …others?

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Gap Analysis: Questions to Address

• Many producers submit data to one publisher• Support for internal and external EPR• Data-sources match well with producers (not necessarily?)• Extended specification addresses this?

• Vocabulary management concern• What if two groups have similar but different vocabularies

• What is the performance of the matching step? What are the scaling limitations?• If matches are limited to vocabulary sets, this may be easily

parallelizable• Use-case exposes subscription requirements

• Query flexibility (on data and on properties that are dynamic) – example solutions have been proposed so far

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Summary

• Wide-Area Sensor Networks data needs arise from their distributed dynamic contexts

• Pub-Sub paradigm addresses “less specific or more general” data requests in distributed systems

• Allows entities to not be known a priori• Implementations needed to prove and refine

the abstractions