Data Sharing Middleware Prototype (DSMP) for Information Dissemination Among Heterogeneous Sources
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Transcript of Data Sharing Middleware Prototype (DSMP) for Information Dissemination Among Heterogeneous Sources
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Data Sharing Middleware Prototype (DSMP) for Information Dissemination
Among Heterogeneous SourcesQuarterly Review Meeting, January 21, 2009
Hairong Qi (PI), University of Tennessee
Xiaorui Wang (co-PI), Seddik Djouadi (co-PI), UT
Oak Ridge National Laboratory*
Oracle Corporation*
Microsoft Research
Rutherford Appleton Laboratory, UK*
* Oracle, Microsoft Research, and ORNL verbal commitments for in-kind support (consulting and research software)
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Contact Information
• Academia– Hairong Qi, 865-974-8527, [email protected], 1508 Middle Dr., 319 Ferris Hall, EECS Department,
University of Tennessee, Knoxville, TN 37996– Xiaorui Wang, 865-974-0627, [email protected], 421 Ferris Hall, UT– Seddik Djouadi, 865-974-5447, [email protected], 307 Ferris Hall, UT– Raghul Gunasekaran, 865-385-5857, [email protected], 536 SERF, UT– Ming Chen, Ying Sun, Samir Sahyoun, Ben Taylor, UT Graduate Students
• Research Laboratories– Frank DeNap, 865-576-8786, [email protected], Oak Ridge National Laboratory, PO Box
2008, MS6085, Oak Ridge, TN 37831– Mallikarjun Shankar, 865-574-2704, [email protected], Oak Ridge National Laboratory, PO
Box 2008, MS6085, Oak Ridge, TN 37831 – Steve Fisher, RAL, [email protected], Rutherford Appleton Laboratory (RAL), UK
• Industry, Private sectors– Dieter Gawlick, Ronny Fehling, Aravind Yalamanchi, 650-560-8706, {dieter.gawlick, ronny.
fehling, aravind.yalamanchi}@oracle.com, Oracle Corporation
– Vijay Dialani, Microsoft Research Center
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Collaborative Team
• Academia– University of Tennessee– Vanderbilt University
• Research Laboratory– ORNL (Oak Ridge National Laboratory, US)– RAL (Rutherford Appleton Laboratory, UK)
• Industry– Microsoft Research– Oracle
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Project Description
• The objective of this project is to develop a data sharing middleware that is able to handle multiple distributed data sources and dynamically changing items, and to assist in real-time information dissemination across multiple agencies for homeland security purposes.
• The ultimate target scenarios are first responders and consequence response at the urban area of Memphis (e.g., Shelby County) with stakeholders including the Fire Department, Weather Services, the E911 Operations Center, Law Enforcement Agencies, etc.
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Minutes from Last Quarterly Review Meeting
• General discussion– Can INFOD behave like an aggregator?
– Can there be registry of registry?
– Human factors of INFOD haven’t been discussed (e.g., no sight, no hearing)
– Clarify the difference between the traditional model and the INFOD model
– Security is an essential issue to be discussed in INFOD
– Different consumers should have different right time to receive the message
– Suggest to demonstrate INFOD in a more confined environment
– Make closer connection of INFOD and the real-time meta-data modeling
– The uniqueness of the proposed plume tracking model should be clarified and emphasized. Instead of moving the sensors, we can think about a sensor grid
– Need to design a scenario where under the same setup, INFOD would make a difference
– Comments on client-side development
• Pre-meeting discussion on Privacy with data sharing systems– Frances Butler and Janet Murrill
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Landscape AssessmentTraditional Model
Publisher
PublisherConsumer
Consumer
Subscriber (Subscription)INFOD RegistryPublisher
Publisher
Consumer
Subscriber/ Consumer
Repository
INFOD Model
• Existing pub/sub systems– Topic-based, Type-based, Content-based– Message-oriented Middleware (MOM)
• Service-oriented system (binding AFTER event)
• Repository – data center, processing center• Static system. Extending the system is
difficult.
• Establishes a framework for info flow• Matches publishers and consumers based
on information needs expressed through subscriptions and limited by properties
• Event-based system (binding BEFORE event)
• Registry - NOT a data (event) repository• Better handling of dynamics. Extensibility is
good. (Vocabulary, e.g., NIEM)
Alerting system
Consumer/Publisher
Right Info Right Person @ Right Time
Push
Pull
Push
Push
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INFOD Components and Setup Procedures
Consumer
Subscription
Data Vocabulary
Data SourceEntry
Property Vocabulary Instance
Creation of resource
Notification (by INFOD registry)
Reference (EPR)
Notification (by Publishers)
INFOD Registry
Entry Resource – not an entry
ConsumerPublisher
Subscriber
Property Vocabulary
PublisherEntry
SubscriberEntry
ConsumerEntry
1. Register community property and data vocabularies in the INFOD registry
2. Define subscriptions binding entities, defining events and which entity needs to be alerted on which other entities presence.
3. Entities register to the INFOD registry
4. Perform matching and notification message sent to matched entities.
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Today’s Presentation Outline
• Development track– Demo: INFOD Web Application - Raghul
Gunasekaran
• Research track– Real-time Metadata Matching in INFOD - Ming Chen – Application Scenario: Chemical Plume Tracking -
Samir Sahyoun
• Discussion of future plan
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Collaborative Opportunities
• Y-12– Privacy policy and NIEM
• ORNL– Testbed setup– Emergency plan
• Oracle– In-kind support
• MSU– Real-time operations support for emergency evacuations
• UT - Emergency Plan Team• All the research findings and software developments are accessible
through public domains, maintained at UT– http://panda.ece.utk.edu/wiki/InfoD
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Project Timeline
• The development of the DSMP (Task 1.2) has been divided into a 4-phase implementation plan. Because of the close collaboration with Oracle, Microsoft Research, ORNL, and RAL, we are able to finish all four phases of prototype development ahead of schedule.
• Phase 1 - simplest scenario with a known data vocabulary and a trivial subscription• Phase 2 - 2 publishers services, 2 consumer services with the addition of property vocabularies• Phase 3 - multiple data vocabularies, publish, consumer, and subscriber services• Phase 4 - a standard notification interface
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4Task 1: Design, Development, and Evaluation of the DSMP
Task 2: Demonstration of the DSMP
Task 2.3: Application scenario 3 - first responders and consequence response
Task 2.2: Application scenario 2 - establishing data correlation
Task 1.2: Prototype design and developmentTask 1.3: Middleware performance evaluation and refinement
Task 2.1: Application scenario 1 - collaborative event analysis
06/07 - 05/08 06/08 - 05/09Tasks
Task 1.1: Literature survey and document study
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Budget Information
• Project budget (June 5, 2007 - May 31, 2009): $400,000
• Spending as of December 31, 2008: $250,759.16
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Commercialization Progress
• The INFOD working group is approached by OGC (Open Geospatial Consortium) to join the consortium. This would help getting more publicity of the product on geospatial and location based services
• Potential feature to Oracle product line
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IP STATUS
• Open source development– Will be available through sourceforge to
stimulate broader participation
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Achievements• Finished all four phases of the prototype development (ahead of schedule)• Papers accepted
– “Control-based real-time metadata matching for information dissemination,” 14th IEEE Int. Conf. on Embedded and Real-Time Computing Sys and App, Taiwan, August 2008. (Acceptance rate: 26%)
– “Dynamic target classification in wireless sensor networks,” Int. Conf. on Pattern Recognition (ICPR), Tampa, FL, December 8-11, 2008.
– “Source localization using stochastic approximation and least squares methods,” 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA'09).
• Students graduated– Y. Sun, Dynamic Target Classification in Wireless Sensor Networks, MS Thesis, Summer 2008.
• Presentations – “INFOD Use Case Scenario & Demo,” Open Grid Forum (OGF), Feb 2008, Boston– “An INFOD Reference Implementation,” Open Grid Forum (OGF), Oct 2007, Seattle
• Papers submitted/in-preparation– “Dynamic cyber-attack detection and classification based on the information dissemination model”– “Plume estimation and tracking using mobile sensors,” To be submitted to IEEE Conference on
Decision and Control 2009.– “INFOrmation Dissemination middleware”
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INFOD Development
Raghul Gunasekaran
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Implementation Model
ORACLE PL/SQL
INFOD Registry Service
OC4JINFOD
Web Service
ORACLE 10g
PL/SQLJava procedures
Client Environment
INFOD Spec.
PostgreSQL Database
Clie
nt L
ibra
ry
Stage 1Stage 2 – INFODweb
Web Management Interface
Tomcat
Publisher App/ Service
Stage 3
ConsumerApp/
Service
Client Applications
ServletsServlets
app.jar : API for communication between applications and INFODweb.
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Mutual Filtering – INFOD Registry
Publisher Constraints
Consumer Constraints
Meta Data
PublisherConsumer
Subscriber/Subscription
Meta Data
Consumer Constraints
Subscriber Constraints
Publisher Constraints
Meta Data
Subscriber Constraints
Note: All constraints and meta data (in identical colors) needs to be evaluated and MUST be true for communication between a publisher and a consumer.
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Meta Data
Subscriber constraint
Consumer constraint
Publisher URI
Meta Data
Subscriber constraintPublisher constraintConsumer URI
Meta Data
Consumer constraint
Publisher constraint
SubscriptionURI
Publisher Subscriber
Consumer
Consumer RowID
Publisher RowID
Publisher Consumer Mapping
DataSource RowID
Consumer RowID
Subscription RowID
Match results
Subscription RowID
Publisher RowID
Publisher Subscription Mapping
Consumer RowID
Subscription RowID
Consumer Subscription Mapping
Mutual Filtering – INFOD Registry
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No. of Entities
Method 1 seconds
Method 2
100 0.4 3.5 sec
200 0.9 < 1 min
400 1.4 1.5 min
600 1.9 2.2 min
800 2.3 3 min
1000 3.8 ~4 min
Method 1 – No intermediate StepMethod 2 – Intermediate Mapping table
Table 1: Mutual Filtering time for meta data with 500 XML elements and no. of entities representing the number of publisher, consumer and subscribers individually.
INFOD Registry – System Performance
Plot 1 – System Scalability
Plot 2 – Performance w.r.t. constraints for 500 entitiesM1 – All constraints; M2 – No constraints on Subscriber
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INFOD ‘Hello, World!’ Example
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Use Case description
Scenario
An accident has occurred, and a bystander reports the event to the E911 center. The E911 center then requests for police, ambulance and/or fire truck to be dispatched based on the current description of the event. As the first officer arrives at the location, the officer reports more details of the accident to the E911 center and also updates on the developments and states of the incident. The E911 center based on the description of the event and the current state of the event alerts the necessary services (fire, medical and police) for action. If resources in a particular region are not sufficient, then the E911 center needs to make a decision in calling for additional resources based on their capabilities and availabilities.
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Use Case descriptionSetup
• The E911 center receives information of any emergency within an area, and based on the incident information it communicates with first responders such as police, hospital, fire and medical services. The E911 center operates closely with the police (control center)
• The police control center instructs and maintains information of its resources in an active duty. Similarly, the fire service and medical services control and command over their resources.
• The E911 center instructs these control centers and the control center communicates to their resources.
The example scenario here is to demonstrate the functioning of INFOD. The scenario might not completely reflect the real world. The goal behind this demo is to have a simpler version of the real world for developers/collaborators/users to understand how INFOD functions.
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First Responder Use Case
E911 Center MedicalEmergency
Service
Fire Service A
Hospital B
Hospital A
Fire Service B
Accident
Observer
Police Control Center
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E911 Center MedicalEmergency
ServicePolice Control Center
Fire Service A
Hospital B
Hospital A
Fire Service B
Accident
Observer
Officer at Accident spot
First Responder Use Case
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INFOD helps • Monitor resources
• All entities registered in the system
• Gain knowledge of the capability/availability of the resource• In terms of property vocabulary instances
• Associate entities (Publishers – Consumers)• Handle incident objects
• What defines as event at the publisher• Policies/ constraints which need to be enforced based on the event
description• Select consumers (dynamic consumers) based on the event
INFOD - First Responder Use Case
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INFOD - First Responder Use Case
E911 Center MedicalEmergency
ServicePolice Control Center
Fire Service A
Hospital B
Hospital A
Fire Service B
Accident
Observer
Consumer
Consumer
Consumer
Publisher
Datasource/ Consumer
DataSource/ Consumer
Consumer
Consumer
ConsumerConsumer
Consumer
INFODRegistry
Consumer
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INFOD in 4 steps1. Register community property and data vocabularies in the INFOD
registry
2. Entities register to the INFOD registry• Create Publisher, Consumer and Subscribers • Create and update property instances – Meta Data
3. Define subscriptions • Property Constraints – on Meta Data• Data Constraints – policies/ event constraints• Dynamic Consumer Constraint – identify consumers based on event
4. Mutual Filtering in the INFOD registry and notification message sent to matched entities.
INFOD - First Responder Use Case
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INFOD Web Services
Step 1 : Register Property and Data vocabularies for a community
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Property Vocabulary
Predicates Description
OrganizationName Name of the entity
OrganizationDescription Description of the entity
OrganizationType Enumeration Type classifying the organization (Police, FireService, MedicalService, Hospital)
Location Location of the organization
Resources Description of the resources
ResourcesType Enumeration Type(FireTruck, Ambulance, Police, Helicopter)
ResourceStatus Enumeration Type on the availability(0 – Active on duty and not available, 1 - Available for emergency)
ResourceLocation Location of the resource
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Data VocabularyPredicates Description
Event Details like number of people involved, number of buildings affected, text description of the event,
EventLocation Location
EventCategory Geo, Met, Safety, Security, Rescue, Fire, Health, Env, Transport, Infra, CBRNE, Other
EventCertainty Very likely, Likely, Possible, Unlikely
EventSeverity Extreme, Severe, Moderate, Minor
ResponseType Shelter, Evacuate, Prepare, Execute, Monitor, Assess, None
ResponseAction Text description of the response action
EventScope Public, Restricted, Private
EventStatus Actual, Exercise, TestUrgency Immediate, Expected, Future, Past
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INFOD Web Services
Step 2 : Create Consumers and Publishers
Property Constraints
for $publishers in fn:collection("$$INFODpublisher") where $ publishers //OrganizationSubUnitName=”RedCross”
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INFOD Web Services
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Property Constraintsfor $publisher in fn:collection("$$INFODpublishers") where $publisher//OrganizationSubUnitName=”E911Center”for $firstresponders in fn:collection("$$INFODconsumers") where $ firstresponders//OrganizationSubUnitName=”RedCross”
Data Constraintsdeclare namespace $data = http://infod.firstrespondernet.com/AlertDataVocabulary; let $msg1 := for $firstresponders where $data:AlertStatus = ‘Actual’ and
$data:EventCategory = ‘CBRNE’ and $data:EventSeverity > ‘Moderate’
return {$data, $data:Instruction = ’Evacuate people in the region’ } let $msg2 := for $firstresponders where $data:capAlertStatus = ‘Actual’ and
$data:EventCategory = ‘Fire’ and $data:EventSeverity > ‘Moderate’
return { $data:Substance, $data:Volume, $data:EventCategory }
Dynamic Consumer Constraint for $firstresponders where $firstresponders//OrganizationSubUnitName=”Police” return msg1 for $firstresponders where firstresponders//OrganizationSubUnitName=”FireService” return msg2
INFOD Web Services
Step 3 : Subscribers and Subscriptions
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INFOD Web Services
Step 4 : Notification Messages
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Integrated Control of Matching Delay and CPU Utilization in Information
Dissemination Systems
Ming Chen, Ben Taylor, Xiaorui Wang
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Information Dissemination System
• Hundreds even thousands of subscriptions are registered;
• Different subscriptions have different priorities and different costs to evaluate
• Updates arrive with unpredictable intervals
• Valuable Information at the Right Time (VIRT).
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Challenges
• The time of reevaluating a subscription may vary significantly.
• Updates may arrive at any inter-arrival intervals
• Reevaluating all subscriptions may cause severe system workload and unacceptable long delays
• We can NOT trigger all subscription reevaluations upon each update.
• We can NOT accurately calculate how many subscriptions to reevaluate to meet with the specified matching delay.
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Solutions
Batching window Hong long?
Subscriptions Hong many?
Our goals: matching delay system throughput
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Architecture• Integrated controller: to control both response time and
CPU utilization• Admission controller: to enforce maximum average
matching interval for low-priority subscriptions
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System Modeling• Relationship between the
matching delay and CPU utilization and the batching window size and job budget.
• System identification (Matlab)
• White noise.
• Randomly choose a job budget such that the CPU utilization > 50%.
Difference Equation
System Model
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Controller Design19.3824 21.2958
0.1289 0.1655IK =− −⎛ ⎞
⎜ ⎟−⎝ ⎠20.4599 24.4225
0.1334 0.1960PK− −⎡ ⎤
=⎢ ⎥−⎣ ⎦
• Larger Q leads to faster response to workload variations;• Larger R makes the system be less sensitive to system noise;• LQR controller is designed by using the Matlab command dlqry to solve the optimization problem.
LQR controller:
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Experimental Setup• Two servers
– Server1: INFOD system, Oracle 10g– Server2: Java code to simulate requests from
consumers, publishers, etc
• JDBC connectivity• Updates follow a Poisson process• Set point
– 2s for matching delay– 0.8 for CPU utilization– 10s for low-priority subscription reevaluation
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Exp 1: Selection of set points of CPU utilization
• a trade-off between the control accuracy and the
system throughputs.
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Exp 2: Control performance
• Show the control performance of the integrated controller by comparing it with the open-loop system.
oscillations Overloaded
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Exp 3: Comparison with baselines
• Delay-only• Util-only
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Exp 4: Admission controller
• Control performance of the admission controller
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Future Work
• Stability analysis on model variations− different platforms;− different execution time of subscriptions;
• More analysis on the admission control
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Chemical Plume TrackingProgress and Future work
Samir S. Sahyoun
Advisor: Seddik M. Djouadi
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Contents
• Summary of Achievements till Last Meeting
• New Results
• Future Work
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Summary of Achievements till Last Meeting
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Source Localization
• Plume source location is estimated using a grid of fixed sensors
• Two approaches have been developed: Non-Linear Least SquaresStochastic Approximation
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Source Localization
Number of SensorsError (m)
Non Linear Least Squares
Stochastic Approximation
10 197 84
20 79 22
Results and more details are in
S. Sahyoun, S.M. Djouadi and H. Qi, Source Localization using Stochastic Approximation and Least Squares Methods, accepted for presentation in the 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA'09).
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Plume Boundary TrackingProcess Block Diagram
• Plume Estimation using State Space Model• Plume Boundary Prediction and Tracking with Mobile Sensors• Closed loop LQR Control of the Sensors
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Plume Boundary Tracking
• Working Paper : S. Sahyoun, S.M. Djouadi and H. Qi, Plume Estimation and Tracking using Mobile Sensors. To be submitted to IEEE Conference on Decision and Control 2009.
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New Results
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Source Localization using Mobile Sensors
• Advantage: Smaller number of sensors needed. Used only four sensors in experiment
• Procedure:
- Sensors estimate the source Location
- Self distribute around estimated source
location
- New estimate is generated
- Process repeated till convergence to
the actual source
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Source Localization using mobile sensors
QuickTime™ and a decompressor
are needed to see this picture.
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Plume Boundary Interpolation
• Objective: Approximate Plume Boundary from Sensor measurements
• Interpolation process is needed to estimate the shape of the plume boundary
• The larger number of sensors the closer the shape to the real plume boundary
• Spline Interpolation technique is used
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Interpolation
-81.28 -81.26 -81.24 -81.22 -81.2 -81.18 -81.16 -81.14 -81.12
36.62
36.63
36.64
36.65
36.66
36.67
36.68
36.69
36.7
36.71
36.72
x(KM)
y(KM)
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Future Work
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Future Work
• Apply Developed Procedure to Different Types of Plume Models (Lagrangian, Euler, etc.)
• Currently sensors controlled independently.• Need: Mobile Sensors Distributed Coordination for
- Source localization rendez-vous problem
- Boundary tracking consensus algorithms
correct, robust, distributed motion coordination
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Cooperative control for distributedsensing and estimation
• Objective: systematic methodology to design and analyze cooperative strategy to control a multi-sensor system to achieve plume tracking
• Challenges:
- limited sensing/communication between sensors
- Information flow, who knows what, when, how,
dynamically changing
- How to coordinate individual sensors into a
coherent whole?
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Design principles for cooperative systems
• Identification of global performance measures:
- Target Tracking
- Boundary Tracking
- Adaptive Interpolation
• Design of interaction laws:
- Rendez- vous diameter of set of neighbors
- Consensus disagreement with neighbors
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Target Tracking: Source Localization
• Objective: How to place sensors/nodes/robots
p1, . . . , pn to track a moving target q0
Rendez-Vous!
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Boundary Tracking: Optimal Deployment
• Example: Strictly convex bodies
• Necessary condition for optimality: optimal interpolations distribute equally the error.
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Boundary Tracking
• Algorithm requirements:
- Initial estimation of boundary
- Initial positions of interpolation points
- Produce an adaptive polygonal approximation of detected boundary
• Sensors cooperatively have to: - Project & optimally place interpolation points on
boundary
- Uniformly distribute themselves according to the arc length
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Future Plan
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Statement of Work
• INFOD enhancement– Develop security and privacy components within INFOD– Real-time response (communication and computation)– Distributed registry
• Coordinate with other SERRI projects on its potential integration with INFOD– MSU’s emergency evacuation project
• School emergency plan system– Integrate INFOD with UT’s emergency plan system– Integrate INFOD with ORNL’s emergency plan system
• Demos– Lab demo: INFOD in plume/smoke boundary tracking– Field demo: UT and/or ORNL emergency evacuation
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INFOD Security and Privacy Feature
• By May 31, 2009– Design document finished.
• June 1, 2009 - Dec. 31, 2009– Implement authentication feature within INFOD– Implement role-based access control
• Jan. 1, 2010 - Jun. 30, 2010– Adopt public-key infrastructure for authentication and validation– Integrate privacy feature into INFOD
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INFOD Registry Enhancement
• By May 31, 2009– We will control both response time and CPU utilization for the INFOD
system. Previous work published in RTCSA only controls response time and so may result in poorly utilized systems.
• June 1, 2009 - Dec. 31, 2009– While our previous work was focused on a single server INFOD registry,
we plan to provide real-time response time guarantee for distributed and federated INFOD registries on multiple servers located in different places.
• Jan. 1, 2010 - Jun. 30, 2010– While our previous work addresses the response time of the INFOD
registry, we plan to address real-time communication between the INFOD registry and various subscribers, consumers, and publishers, in order to provide an end-to-end real-time information dissemination system.
– We plan to deploy and test our system in a small-scale real application environment for demonstrations.
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Integration with other SERRI Projects
• University of Southern Mississippi– Evacuation from sports arena
• National Transportation Research Center (NTRC)– Redirect traffic– Oscar Frensy
• Mississippi State University– Real-time operations support for emergency evacuations– Li Zhang
• Sensorpedia– Display information gathered from sensors– Bryan Gorman ([email protected])
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Integration with UT and/or ORNL Emergency Plan System
• Existing system– Can handle 3 subgroups
• New system– Code blue phones – Communication NXT
• Emergency call bridge• Reverse #1 function
• Problem– Still manual setup– A call tree
Managed by UT-Battelle for the U.S. Department of Energy – Supporting the Department of Homeland Security
Integration with Plume Analysis
• By May 31, 2009 - We will apply the plume tracking procedure developed to different plume models.
• June 1, 2009 - Dec. 31, 2009 - Previous work focused on controlling the sensors independently. We plan
to develop distributed consensus and coordination algorithms for sensors for both plume source localization and tracking.
• Jan. 1, 2010 - Jun. 30, 2010 - Application, testing and validation of distributed sensor paradigms on
different plume models. - We plan to implement and test our system in a small scale lab experiment.
Managed by UT-Battelle for the U.S. Department of Energy – Supporting the Department of Homeland Security
Lab Demo: Plume Tracking
Managed by UT-Battelle for the U.S. Department of Energy – Supporting the Department of Homeland Security
Field Demo: Emergency Evacuation at Neyland Stadium
Managed by UT-Battelle for the U.S. Department of Energy – Supporting the Department of Homeland Security
ScheduleQ2 Q3 Q4 Q1 Q2 Q3 Q4
Task 1: INFOD enhancement
Task 2: Integration of INFOD
Task 3: Lab and field demo
Task 3.2: Emergency evacuation at Neyland Stadium demo
Task 2.2: With other appropriate, feasible applications - Plume tracking
Task 1.2: Security and privacy component developmentTask 1.3: Distributed registry
Task 2.1: With other SERRI projects
Task 2.3: With UT and/or ORNL emergency plan system
Task 3.1: Lab demo: plume tracking
06/09-12/09 01/10 - 06/10Tasks
Task 1.1: Real-time response (communication and computation)