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US/UK International Technology Alliance(ITA)
US/UK International Technology Alliance(ITA)
John GowensARL Collaborative Alliance Manager
Jack Lemon MoD Collaborative Alliance Manager
Dinesh Verma & David WatsonProgram Managers
Network and Network and Information SciencesInformation Sciences
IBM
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The ITA VisionThe ITA Vision
Creating an international collaborative research culture Academia, Industry, Government in US and UK Innovative multidisciplinary approaches
Developing ground-breaking fundamental science Empower innovators Develop understanding of the root cause of military technical
challenges
Making an impact on coalition military effectiveness Focus on key problems with a critical mass of researchers Gain synergies from UK/US alignment Innovative transition model
A US/UK Alliance conducting collaborative research focused on improving coalition operations by:
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U.S.Gov.
Industry
Academia
U.K.Gov.
INDUSTRY9. BBNT Solutions LLC
10.The Boeing Corporation
11.Honeywell Aerospace Electronic Systems
12. IBM Research
13.Klein Associates
ACADEMIA1. Carnegie Mellon University
2. City University of New York
3. Columbia University
4. Pennsylvania State University
5. Rensselaer Polytechnic Institute
6. University of California Los Angeles
7. University of Maryland
8. University of Massachusetts
INDUSTRY8. IBM UK
9. LogicalCMG
10.Roke Manor Research Ltd.
11.Systems Engineering& Assessment Ltd.
ACADEMIA1. Cranfield University, Royal Military
College of Science, Shrivenham
2. Imperial College, London
3. Royal Holloway University of London
4. University of Aberdeen
5. University of Cambridge
6. University of Southampton
7. University of York
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ITA Team OverviewITA Team Overview
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Technical AreasTechnical Areas
1. Network Theory
Enable the formation/operation of ad hoc coalition teams
2. Security Across a System of Systems
Fundamental underpinnings for adaptive networking and security to support complex system-of-systems
3. Sensor Information Processing and Delivery
Sensor information processing/delivery from distributed sensor networks to support enhanced decision-making
4. Distributed Coalition Planning and Decision Making
Understand and support complex human, social, and technical interactions in distributed coalition teams
Goal: Enhancing distributed, secure, and flexible decision-making to improve coalition operations
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International Technology Alliance in International Technology Alliance in Network and Information SciencesNetwork and Information Sciences
Collaborative Alliance Managers/Consortium Managers Jay Gowens (ARL) Jack Lemon (MoD) Dinesh Verma (IBM) Dave Watson (IBM-UK)
International Technology Alliance in International Technology Alliance in Network and Information SciencesNetwork and Information Sciences
Collaborative Alliance Managers/Consortium Managers Jay Gowens (ARL) Jack Lemon (MoD) Dinesh Verma (IBM) Dave Watson (IBM-UK)
Security Across a Security Across a System-of-SystemsSystem-of-Systems
Trevor Benjamin (Dstl)Trevor Benjamin (Dstl)Greg Cirincione (ARL)Greg Cirincione (ARL)John Mcdermid (York)John Mcdermid (York)Dakshi Agrawal (IBM)Dakshi Agrawal (IBM)
Security Across a Security Across a System-of-SystemsSystem-of-Systems
Trevor Benjamin (Dstl)Trevor Benjamin (Dstl)Greg Cirincione (ARL)Greg Cirincione (ARL)John Mcdermid (York)John Mcdermid (York)Dakshi Agrawal (IBM)Dakshi Agrawal (IBM)
Network TheoryNetwork Theory
Ananthram Swami (ARL)Ananthram Swami (ARL)Tom McCutcheon (Dstl)Tom McCutcheon (Dstl)Don Towsley (U Mass)Don Towsley (U Mass)Kang-Won Lee (IBM)Kang-Won Lee (IBM)
Network TheoryNetwork Theory
Ananthram Swami (ARL)Ananthram Swami (ARL)Tom McCutcheon (Dstl)Tom McCutcheon (Dstl)Don Towsley (U Mass)Don Towsley (U Mass)Kang-Won Lee (IBM)Kang-Won Lee (IBM)
Sensor Information Sensor Information ProcessingProcessing
Tien Pham (ARL)Tien Pham (ARL)Gavin Pearson (Dstl)Gavin Pearson (Dstl)
Thomas La Porta (PSU)Thomas La Porta (PSU)Vic Thomas (Honeywell)Vic Thomas (Honeywell)
Sensor Information Sensor Information ProcessingProcessing
Tien Pham (ARL)Tien Pham (ARL)Gavin Pearson (Dstl)Gavin Pearson (Dstl)
Thomas La Porta (PSU)Thomas La Porta (PSU)Vic Thomas (Honeywell)Vic Thomas (Honeywell)
Distributed Coalition Distributed Coalition PlanningPlanning
Jitu Patel (Dstl)Jitu Patel (Dstl)Mike Strub (ARL)Mike Strub (ARL)
Nigel Shadbolt (SHamp)Nigel Shadbolt (SHamp)Graham Bent (IBM)Graham Bent (IBM)
Distributed Coalition Distributed Coalition PlanningPlanning
Jitu Patel (Dstl)Jitu Patel (Dstl)Mike Strub (ARL)Mike Strub (ARL)
Nigel Shadbolt (SHamp)Nigel Shadbolt (SHamp)Graham Bent (IBM)Graham Bent (IBM)
Policy Based Security Management
Calo, IBMCalo, IBM
Policy Based Security Management
Calo, IBMCalo, IBM
Energy Efficient Security
Architectures and Infrastructures
Paterson, Royal Paterson, Royal HollowayHolloway
Energy Efficient Security
Architectures and Infrastructures
Paterson, Royal Paterson, Royal HollowayHolloway
Trust and Risk Management in
Dynamic Coalition Environments
Clark, YorkClark, York
Trust and Risk Management in
Dynamic Coalition Environments
Clark, YorkClark, York
Theoretical Foundations for
Analysis/Design of Wireless and Sensor
Networks
Towsley, U MassTowsley, U Mass
Theoretical Foundations for
Analysis/Design of Wireless and Sensor
Networks
Towsley, U MassTowsley, U Mass
Interoperability of Wireless Networks
and Systems
Lee, IBMLee, IBMHancock, RMRHancock, RMR
Interoperability of Wireless Networks
and Systems
Lee, IBMLee, IBMHancock, RMRHancock, RMR
Biologically-Inspired Self-Organization in
Networks
Lio, CambridgeLio, CambridgePappas, IBMPappas, IBM
Biologically-Inspired Self-Organization in
Networks
Lio, CambridgeLio, CambridgePappas, IBMPappas, IBM
Quality of Information of Sensor Data
Bisdikian, IBMBisdikian, IBM
Quality of Information of Sensor Data
Bisdikian, IBMBisdikian, IBM
Task-Oriented Deployment of Sensor Data
Infrastructures
La Porta, Penn StateLa Porta, Penn State
Task-Oriented Deployment of Sensor Data
Infrastructures
La Porta, Penn StateLa Porta, Penn State
Complexity Management of
Sensor Data Infrastructures
Szymanski, RPISzymanski, RPI
Complexity Management of
Sensor Data Infrastructures
Szymanski, RPISzymanski, RPI
Mission Adaptive Collaborations
Poltrock, BoeingPoltrock, Boeing
Mission Adaptive Collaborations
Poltrock, BoeingPoltrock, Boeing
Cultural Analysis
Sieck, Klein AssocSieck, Klein Assoc
Cultural Analysis
Sieck, Klein AssocSieck, Klein Assoc
Semantic Integration & Coalition Planning
Smart, SouthhamptonSmart, SouthhamptonBraines, IBMBraines, IBM
Semantic Integration & Coalition Planning
Smart, SouthhamptonSmart, SouthhamptonBraines, IBMBraines, IBM
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Accomplishments 06-07Accomplishments 06-07
Key US/UK Collaborations Enabled
• Policy ManagementSloman (Imperial) Bellovin (Columbia) Calo/Lobo (IBM-US)
• Biologically Inspired Techniques Lio (Cambridge)Seshan (CMU)Towsley (U. Mass)
• Mission Specific Sensor Network Configuration
Leung (Imperial)La Porta (Penn State)
• Operations Analysis using Second Life
Wagget (IBM-UK)US Military Academy (Graham)
• Semantic Battlespace Infosphere Shadbolt (Southampton)
Hendler (RPI)
Technical Results Attained
• Policy based self managed cells for coalition operations
• Wireless sensor network design based on human circulatory systems.
• Models to analyze properties of MANETs in non-asymptotic case
• Quality of Information calculus to improve detection methods for sensor network
• Lightweight scalable infrastructure for sensor information collection and dissemination
• Multi-player online role playing game based Paradigms to model coalition operations
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Network Theory(Towsley U. Mass, Lee IBM-US)
Network Theory(Towsley U. Mass, Lee IBM-US)
Fundamental underpinnings for adaptive networking to support complex system-of-systems and ad hoc coalition teams
• Theoretical foundations for design of wireless and sensor networks (Towsley, U. Mass)
• Interoperability of wireless networks and systems (Hancock, RMR/Lee IBM-US)
• Biologically-Inspired self-organization in networks (Lio Cambridge/Pappas IBM-US)
Finding hidden community structure and motifs in networks
Simple biological network
LethalSlow-growth
Non-lethalUnknown
• Mathematical models of interoperation to enable design of coalition networks
• Analysis of community patterns in biological networks and their applications to wireless systems.
• Models analyzing MANETs and performance of protocols
FY08-09 Objectives
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Security Across a System-of-Systems(Mcdermid York, Agrawal IBM-US)
Security Across a System-of-Systems(Mcdermid York, Agrawal IBM-US)
Fundamental underpinnings for adaptive security to support complex system-of-systems and ad hoc coalition teams
• Policy based security management (Calo, IBM-US)
• Energy efficient security architectures and infrastructures (Paterson, Royal Holloway)
• Trust and risk management in dynamic coalition environments (Murdoch, York)
• Fixed infrastructure free security mechanism
• Enablement of secure dynamic communities of interest
• Identity based trust management systems for MANETs
FY08-09 Objectives
Special-purpose Modeling Notations
State Machines, ACLs, Other ConfigLanguages
Natural Language Vocabulary
Battle Space Ontologies
Natural Language
Specifications
Abstract Policies
Cross-cutting Interaction
Models
Platform-specific
Configurations
Policy Specification
Layer
Abstract PolicyLayer
Concrete PolicyLayer
Implementation and Configuration
Layer
Real-time Updates
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Sensor Information Processing/Delivery(La Porta Penn State, Thomas Honeywell)
Sensor Information Processing/Delivery(La Porta Penn State, Thomas Honeywell)
Sensor information processing and delivery from distributed multi-modal sensor systems within adaptive sensor networks• Quality of Information of sensor data
(Bisdikian, IBM-US)
• Task-oriented deployment of sensor data infrastructure (La Porta, Penn State)
• Complexity management of sensor data infrastructure (Szymanski, RPI)
• Quality of information representations to facilitate fusion at multiple levels
• Adaptive data infrastructures based on mission requirements and sensor-mission matching algorithms
• Information overload reduction techniques for military sensor networks
FY08-09 Objectives
Service Oriented Architecture for Sensor Networks
Quality of Information (7)
Deployment (8)Management (9)
QoIUpdated
Configuration
UpdatedConfiguration
Target Operating point
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Distributed Coalition Planning/Decision-Making(Shadbolt Southampton, Bent IBM-UK)
Distributed Coalition Planning/Decision-Making(Shadbolt Southampton, Bent IBM-UK)
Planning and decision-making that takes into consideration the human, social, and technical interactions anticipated in distributed coalition teams• Mission adaptive collaborations
(Poltrock, Boeing)
• Cultural analysis (Sieck, Klein Assoc)
• Shared situational awareness/ Semantic Battlespace Infosphere (Waggett, IBM-UK)
• Improved understanding of multinational planning and decision making
• Agile, adaptive collaboration among humans and software agents engaged in collaborative decision-making
• Semantic Integration and Collaborative Planning
FY08-09 Objectives
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Project 1: Theoretical foundations for design of
wireless and sensor networks Project 1: Theoretical foundations for design of
wireless and sensor networks
• Team– U. Mass, BBN, ARL, Imperial, Cambridge, SEA, RMR,
Dstl• Goal
– determine fundamental performance limits in military mobile multi-hop ad hoc wireless networks.
– develop robust optimization framework for the design of resource allocation algorithms in such networks.
• Key US/UK Collaboration– U. Mass and Imperial for cooperative diversity using
MIMO antennas– SEA and U. Mass collaboration with potential
visits/training experiences.
• Key 2006 Achievements– Analysis of power reduction attributes in cooperative
diversity– Analysis of 1-D and 2-D arrays with duty-cycling
• Key Objectives 2007-2009– Analysis of Cooperative Networking– Analysis of Robust optimization of routing and rate
control– Protocols for Mission Specific Network Configuration
• joint task with TA-3 Project 8
• Military Relevance– Understanding characteristics of networks is
fundamental necessity for NCO– Results will lead to better protocols for MANETs, and
better network design/planning tools. Power reduction by cooperative transmission
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Project 2: Interoperability of Wireless Systems and Networks
Project 2: Interoperability of Wireless Systems and Networks
• Team– IBM, Honeywell, UCLA, CUNY, IBM-UK, ARL, Imperial,
Cambridge, RMR, Dstl• Goal
– Investigate fundamental technical issues related to the interoperation of heterogeneous wireless networks and systems
• US/UK Collaborations– Imperial, IBM UK and IBM US for MANET monitoring – Cambridge and IBM US on Inter Domain Routing– Imperial, IBM-US and UCLA on Epidemic Data
Dissemination
• Key Achievement for 2006– Analysis of capacity gains using Opportunistic Spectrum
Scavenging in Coalition Networks– Investigated scalable and efficient data dissemination in
MANETs using a novel network coding technology, and improved data delivery ratio while reducing the overhead.
– Developed a formal inter-domain meta-routing framework for multi-domain MANETs.
– Extended network coding models for multi-party and multi-hop network coding.
– Formulation of finite MANETs in terms of static equivalent graphs for analysis
• 2007-2008 Objectives– Task 1: Network Monitoring and Troubleshooting in MANETs– Task 2: Inter-domain Wireless Routing in MANETs– Task 3: Data Delivery Using Controlled Epidemic
Multicasting
• Military Relevance– Analysis of network characteristics of coalition environments.
Vehicular ad-hoc network
(large scale, high duty cycle)Infantry
section (small scale, power constrained)
Infantry section
(coalition partner)
Each different network will have different performance characteristics, access
policies, operational goals, …
The different network requirements lead to different internal MANET routing mechanisms
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Project 3: Biologically Inspired Self-Organization in Wireless Networks
Project 3: Biologically Inspired Self-Organization in Wireless Networks
• Team– IBM, CMU, U. Mass, BBN, ARL, Cambridge, RMR, Dstl
• Goal– Leverage millennia of evolution of biological systems to design
better wireless networks
• US/UK Collaborations– Cambridge, CMU and IBM US working together on BioInspired
Topology Control Mechanisms – Cambridge, U. Mass and BBN working together on dynamic
graphs– Cambridge, U. Mass, ARL and IBM working together on
organization of BioWire.
• Key Achievements for 2006– Developed algorithms for identification of hidden patterns in
communication graphs. – Organization of BioWire 2007 as a catalyst for biologically
inspired approaches– Using the Human Circulation Model to design efficient duty-
cycling wireless sensor networks
• 2007-2008 Objectives– Task 1: Mobility Models for Dynamic Graphs and Information
Dissemination– Task 2: MANET Topology Control
• Military Relevance– Develop self-organizing systems that are as resilient as
biological systems.
Simple biological network
LethalSlow-growth
Non-lethalUnknown
Small Hop Count Wireless Network
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Project 4: Policy based Security ManagementProject 4: Policy based Security Management
• Team – IBM, Honeywell, Columbia, ARL, Imperial, Cambridge,
CESG, • Goal
– Automate the process of enforcing and validating operational security policies into coalition networks.
• US/UK Collaborations– Imperial and IBM UK working together on developing
policy analysis and refinement algorithms.– Cambridge and Columbia working together on policy
enforcement in coalition MANETs.
• Key Achievements for 2006– Developed architecture for self-managing secure cells
in dynamic environments. – Specifications for formal representations of security
policies in coalition networks.
• 2007-2008 Objectives– Task 1: Policy Refinement Algorithms– Task 2: Foundations for Policy Specification and
Analysis– Task 3: Policy based Enablement of Secure Dynamic
Communities– Task 4: Distributed Policy Enforcement for Secure
Information Flows
• Military Relevance– Simplify compliance with security policies of coalition
networks.
1. User Intuitive notation
Feedback on feasibility
2. Safety, liveness goals
3. Abstract state machine
4. Concrete state machine
5. System and concrete policies
compilation
analysis
enforceability
refinement
6. Policy negotiation
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Project 5: Energy Efficient Security ArchitecturesProject 5: Energy Efficient Security Architectures
• Team– IBM, UMD, CUNY, ARL, Royal Holloway, York, Dstl
• Goal– Enable security for information flows in flexible dynamic
coalitions with multiple communities, dynamic node mobility and constrained power.
• Pioneer use of new security infrastructures, key management techniques and lightweight security mechanisms/protocols in dynamic, mobile, ad hoc military networking environments
• Understand interactions between security and heterogeneity in military networking environments
• make security an enabler rather than a hindrance for collaboration in dynamic CoIs
• US/UK Collaborations– RHUL, UMD and IBM US working together on applying
threshold cryptography to MANETs.
• Key Achievements for 2006– Developed techniques for inter-operation of entities with
different trust authorities to enable dynamic coalition formation.
– Developed usage models and scenarios for Identity based keying in MANET environments.
• 2007-2008 Objectives– Threshold approaches to building security services in
MANETs – Lightweight security infrastructures for MANETs– Mechanisms enabling secure information flows
• Military Relevance– Efficient security protocols for better efficiency of coalition
networks
TA
Secure channel
Authentic public parameters
Alice’s ID
X
Info Flow
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Project 6: Trust and Risk in Coalition EnvironmentsProject 6: Trust and Risk in Coalition Environments
• Team– IBM, UMD, ARL, York, Holloway, Cranfield,
Dstl• Goal
– Incorporate the concept of acceptable trust in coalition operations to make security and enabler of coalition operations, as opposed to a hinderance.
• US/UK Collaborations– York, RHUL and IBM US working together
on development of trust and risk calculus.
• Key Achievements for 2006– Developed techniques for fuzzy logic based
risk calculation and access control.
• 2007-2008 Objectives– Dynamic Distributed Risk Estimation in
MANETs – Risk Calculations
• Military Relevance– Enable an understanding of trust and risk
trade-offs in coalition operations InitialBootstrapping
AdversaryModel
AdversaryModel AdversaryModels
TrustAlgebra
To trust or not to trust? That is the
question
Trust level is high
Armed PersonApproaching
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Project 7: Quality of Information in Sensor NetworksProject 7: Quality of Information in Sensor Networks
• Team– IBM, Honeywell, UCLA, CUNY, UMD, ARL, Imperial,
Dstl • Goal
– Develop technologies to describe, analyze and estimate the quality of information delivered by a sensor network.
• How good is the sensor information and how is it affected by network and sensor characteristics
• US/UK Collaborations– Imperial and Honeywell working together on
development of Impact of routing and energy on QoI.– Imperial and IBM working together on QoI Calculus
• Key Achievements for 2006– Developed statistical and physical model techniques for
in-network blind calibration. – Analysis of relationship between QoI and Sensor
Sampling Policies– Impact of routing on timeliness of information.
• 2007-2008 Objectives– QoI Specification and Analysis Framework – Sensor characteristics and QoI – QoI and Network Services– QoI Calculus for Event Detection
• Military Relevance– Improvements in the quality of information delivered by
the sensor network infrastructureContextual or multiscale information
Another modality on the same node
Nodes of Same Altitude or Depth
Proximate Nodes
Measurements at same time previous day
Recent Measurements
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Project 8: Mission Oriented Sensor ConfigurationProject 8: Mission Oriented Sensor Configuration
• Team– Penn State, CUNY, IBM, ARL, Aberdeen,
Imperial, IBM-UK, Dstl• Goal
– Develop technologies to capture mission requirements and to configure, provision and optimize sensor information fusion infrastructure to best support the mission requirements.
• US/UK Collaborations– IBM UK and IBM US working on applying
message fabric infrastructure to sensor networks.
• Key Achievements for 2006– Developed algorithms for optimal
assignment of sensors to missions – Pioneered use of message queue
infrastructure for sensor information processing.
• 2007-2008 Objectives– Sensor Mission Matching– Mission Specific Network Configuration– Direction and Dissemination
• Military Relevance– Optimal use of resources to get “best” and
most important intelligence in a timely manner to the right parties
Mission
OperationOperation
TaskTask
Task
Component
System
Platform
CapabilityCapability
Capability
Capability requirements to perform tasks to standard
under given conditions
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Project 9: Complexity Reduction of Sensor DeploymentsProject 9: Complexity Reduction of Sensor Deployments
• Team– RPI, IBM, CUNY, ARL, Aberdeen,
Southampton, IBM-UK, Dstl• Goal
– Develop technologies to capture mission requirements and to configure, provision and optimize sensor information fusion infrastructure to best support the mission requirements.
• Key Achievements for 2006– Developed paradigm for sensor as a
distributed network database – Development of opportunistic routing
mechanisms for sensor networks.
• 2007-2008 Objectives– User Oriented Information Processing
and Retrieval Paradigms– Semantically Mediated Data Fusion– Root Cause Analysis and Overload
Protection
• Military Relevance– Simplify the management and
interpretation of sensor information by the warfighter during tactical operations.
Service Composition
Optimized Deployment
Component Discovery
Mission Tasking
Process Choreography
Tactical
Info
rmatio
nS
enso
r Taskin
g
Integ
ration
(En
terprise S
ervice Bu
s)
Qo
S L
ayer (Secu
rity, Man
agem
ent &
Mo
nito
ring
Infrastru
cture S
ervices)
Data A
rchitectu
re (meta-d
ata) & B
usin
ess Intellig
ence
Go
vernan
ce
Sensors
Systems domain
Network domain
C C C C C C C
CMCCentral Mission ControlCentral Banking Authority
Budget AllocationsMapping of MissionsInto Budget Decisions
Mission Commanders
Bids for services
Allocation Decisions
Bidding Strategies
AllocationPolicies
Sensor Networks
SN
SN SN
SN
SN
SN
SN
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Project 10: Mission Adaptive CollaborationsProject 10: Mission Adaptive Collaborations
• Team– Boeing, CMU, CUNY, ARL, Aberdeen, IBM-UK, Dstl
• Goal– Develop and validate a theory for agile, adaptive collaboration
among humans and software agents .
• US/UK Collaboration– Aberdeen and CMU have significant rotation and cross-
collaborative activities– IBM UK in significant studies with US Military Academy, West
Point
• Key Achievements for 2006– Second Life Metaverse system based validation for Recognition
Primed Decision Model – Analysis and Models of of Variability in Complex Collaborative
Processes.
• 2007-2008 Objectives– Models of Hybrid Human Agent Teams: Agent support for ad
hoc adaptive teamwork• Perform task analysis of military tasks• Develop models of hybrid human-agent teamwork• Develop agent technologies to implement the models
– Computer Mediated Social Interactions• Establish game/simulation environments where people and
agents can collaborate• Develop analysis methods that reveal team activities and
context
• Military Relevance– Enable the war fighters in coalition to understand when and
how to collaborate and use software assistance for improved effectiveness.
Analyze Military
Task
DevelopReasoning
Model
Agent implementing
Reasoning Model
EvaluateAccuracyOf Model
Validate withHuman Team
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Project 11: Cultural AnalysisProject 11: Cultural Analysis
• Team– Klein, Columbia, Boeing, ARL, Cranfield,
IBM-UK, SEA, Dstl • Goal
– Understand the differences in cultural behavior between US and UK and mitigate the frictions of culture in coalition operations
– advance the state of the art in cultural analysis in cognition, language, social interaction to improve coalition operations.
• Key Achievements for 2006– New methodology for cultural network
analysis was developed.
• 2007-2008 Objectives– Cultural modelling of Planning and Intent– Analysis of culturally dependent
communication patterns
• Military Relevance– mitigates the friction of culture in coalition
operations.
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Project 12: Shared Situational Awareness Project 12: Shared Situational Awareness
• Team– Boeing, RPI, Honeywell, Klein, Southamton, IBM-UK,
Dstl,• Goal
– Develop technologies and techniques to improve coalition interoperability, information exploitation, shared understanding and collaborative planning through semantic integration, improved information representation and formal plan representation.
• US/UK Collaboration– Southampton and RPI working together on semantic
technologies– Boeing and IBM-UK working on collaborative planning
model
• 2007-2008 Objectives– Semantic Integration and Interoperability– Plan representation with collaborative planning model
• Military Relevance– Improved situational awareness and better planning
tools.
Tas
k 1
Task 2
Information RepresentationShared Understanding
Semantic Integration Information Exploitation
Information Exchange Communication & Collaboration
Integrative Framework
for Semantic Integration
Rules(Adaptive Selection, Automatic
Parameterization)
Empirical Evaluation
Semantic Integration Techniques
MAFRA
GLUE PROMPT
Others
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Project 3Project 3
Biologically Inspired Self-Organization in Wireless Networks
Champion: Pietro Lio, Cambridge and Vasilieos Pappas, IBM
CMU
Roke Manor Research Ltd
University of Cambridge
IBM Research
BBN
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Project 3 TeamProject 3 Team
• US– Academia
• Srini Seshan, CMU• Don Towsley, Jim Kurose, U. Massachusetts
– Industry• Vasilieos Pappas, Kang-won Lee, Asser Tantawy, IBM• Prithwish Basu, BBN
– Government• Ananthram Swami, ARL
• UK– Academia
• Pietro Lio, Jon Crowcoft, Cambridge – Industry
Mark West, RMR– Government
• Abigail Solomon, Tom McCutcheon, Dstl
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Project 3 OverviewProject 3 Overview
• Goal– Leverage millennia of evolution of biological systems to design better wireless networks
• US/UK Collaborations– Cambridge, CMU and IBM US working together on BioInspired Topology Control
Mechanisms – Cambridge, U. Mass and BBN working together on dynamic graphs– Cambridge, U. Mass, ARL and IBM working together on organization of BioWire.
• Key Achievements for 2006– Developed algorithms for identification of hidden patterns in communication graphs. – Organization of BioWire 2007 as a catalyst for biologically inspired approaches– Using the Human Circulation Model to design efficient duty-cycling wireless sensor networks
• 2007-2008 Objectives– Task 1: Mobility Models for Dynamic Graphs and Information Dissemination– Task 2: MANET Topology Control
• Military Relevance– Develop self-organizing systems that are as resilient as biological systems.
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Project 3 AchievementsProject 3 Achievements
Key Collaborations Enabled
Cambridge (Lio), CMU (Seshan) and IBM US (Pappas) --- bio-inspired topology control mechanisms
U. Mass (Towsley), Cambridge (Crowcroft/Lio), and BBN (Redi) --- dynamic graphs
Biologically-inspired techniques for resilient self-organizing networks
ITA-Sponsored Biowire 2007 Workshop
Focus on bio-inspired design of wireless networks
Organized by ARL-Dstl-IBM-Cambridge with over 50 confirmed speakers
University of Cambridge, 2-5 April
Using the Human Circulation Model to design efficient duty-cycling wireless
sensor networks
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Mobility Models for Dynamic GraphsMobility Models for Dynamic Graphs
• Problem – What are representative models for dynamic
graphs representing MANETs?
• Hypothesis– Dynamic graphs representing MANETs represent
topology patterns and information dissemination models that are isomorphous to those found in epidemic spread of viruses.
• Validation of Hypothesis – Obtain traces of mobility of dynamic wireless
networks from U. Massachusetts DieselNet Infrastructure
– Obtain mathematical models representing movement and information dissemination patterns.
– Compare patterns to those obtained from epidemiology patterns found in Cambridge research efforts.
– Determine Similarities and Differences
• If hypothesis can be validated– Apply distributed models of epidemic propagation
to disseminate information in military networks.
Simple biological network
LethalSlow-growth
Non-lethalUnknown
Small Hop Count Wireless Network
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MANET Topology ControlMANET Topology Control
• Problem – How can we develop a good topology
representing the structure of MANETs and wireless sensor Network?
• Assertion– Synchronization Pulses created by
Circulatory Systems provide a good approach for energy-efficient duty-cycling in wireless sensor networks.
– Models for epidemiological propagation and assembly of circulatory systems provides mechanisms for distributed self-organization
• Approach – Develop a network design algorithm
modeled after circulatory system. – Obtain mathematical models representing
growth of biological networks. – Adapt biological models to analyze
topology formation in MANETs and compare effectiveness to non-biological approaches.
• e.g. Ant Colony Optimization
Heart
Cells
Artery
Vein
Capillary
Blood flow
Lungs
Mammalian Circulatory System
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Security Across a System-of-Systems(Mcdermid York, Agrawal IBM-US)
Security Across a System-of-Systems(Mcdermid York, Agrawal IBM-US)
Fundamental underpinnings for adaptive security to support complex system-of-systems and ad hoc coalition teams
• Policy based security management (Calo, IBM-US)
• Energy efficient security architectures and infrastructures (Paterson, Royal Holloway)
• Trust and risk management in dynamic coalition environments (Murdoch, York)
• Fixed infrastructure free security mechanism
• Enablement of secure dynamic communities of interest
• Identity based trust management systems for MANETs
FY08-09 Objectives
Special-purpose Modeling Notations
State Machines, ACLs, Other ConfigLanguages
Natural Language Vocabulary
Battle Space Ontologies
Natural Language
Specifications
Abstract Policies
Cross-cutting Interaction
Models
Platform-specific
Configurations
Policy Specification
Layer
Abstract PolicyLayer
Concrete PolicyLayer
Implementation and Configuration
Layer
Real-time Updates
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Columbia University
Imperial College
University of Cambridge
Project 4Project 4
Policy based Security Management
Champion: Seraphin Calo, IBM
Honeywell Aerospace Electronic SystemsIBM Research
31
Project 4 TeamProject 4 Team
• US– Academia
• Steve Bellovin, Columbia– Industry
• Seraphin Calo, Jorge Lobo, IBM• Thomas Markham, Honeywell
– Government• Greg Cirincione, ARL
• UK– Academia
• Jon Crowcoft, Cambridge • Morris Sloman, Emil Lupu, Imperial
– GovernmentChris Lloyd, CESG
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Project 4 OverviewProject 4 Overview
• Goal– Automate the process of enforcing and validating operational security policies into
coalition networks.
• US/UK Collaborations– Imperial and IBM UK working together on developing policy analysis and
refinement algorithms.– Cambridge and Columbia working together on policy enforcement in coalition
MANETs.
• Key Achievements for 2006– Developed architecture for self-managing secure cells in dynamic environments. – Specifications for formal representations of security policies in coalition networks.
• 2007-2008 Objectives– Task 1: Policy Refinement Algorithms– Task 2: Foundations for Policy Specification and Analysis– Task 3: Policy based Enablement of Secure Dynamic Communities– Task 4: Distributed Policy Enforcement for Secure Information Flows
• Military Relevance– Simplify compliance with security policies of coalition networks.
33
Policy Life CyclePolicy Life Cycle
34
Policy Operation in CoalitionsPolicy Operation in Coalitions
Commanders Specifies
Operational Policies
Commanders Specifies
Operational Policies
US: Share Mission-Critical Information on Need to Know Basis
UK: Isolate Coalition Traffic from UK only traffic
Policy System Translates
Operational PoliciesInto Machine-Readable
Operational Policies
Policy System Translates
Operational PoliciesInto Machine-Readable
Operational Policies
Policy System Analyzes
Operational Policiesfor conflicts/errors
Policy System Analyzes
Operational Policiesfor conflicts/errors
Policy System Refines
Operational PoliciesInto
Deployable Policies
Policy System Refines
Operational PoliciesInto
Deployable Policies
Policy System Distributes
Deployable Policiesto
MANET Devices
Policy System Distributes
Deployable Policiesto
MANET Devices
MANET Devices EnforcePolicies
MANET Devices EnforcePolicies
Enforcement Support, Black Box device Capture
US: XML Representation
UK: XML RepresentationUK: Isolation Not Feasible: require additional Comm. Vehicle
US: Policy possible with known configuration
US: CIM-SPL/XACML representation of access control, Encryption and communication policies
UK: CIM-SPL representation of Comm Equipment Access Filters
A distributed messaging system designed for MANETs
Policy System Validates
Compliance In Post-Mortem
Policy System Validates
Compliance In Post-Mortem
Compare Black Box and Monitored Information to Policy
Policy Systemupdates
Devices to PoliciesEffective Post Operation
Policy Systemupdates
Devices to PoliciesEffective Post Operation
Enforcement Support, Black Box device Capture
35
Research ChallengesResearch Challenges
DefinitionDefinition TranslationTranslation AnalysisAnalysis
RefinementRefinement
DeploymentDeploymentEnforcementEnforcement
AuditAudit
ResetReset
Formal Representations Available for some Policy Models
Refinement Algorithms Deconflicting Algorithms
Refinement for Security Policies
Coalition Compatibility Analysis
Deployment in MANET environmentsAvailable for Wired EnvironmentsC&N CTA MANET Infrastructure
Enforcement with Power Constraints
Incorporating Experiences
Enabled by Refinement
Red – Unsolved Research Problems
Green – Known in State of Art
Policy Models and Languages
Policy Auditors & Validators
Enforcement understood in wired networks
Available for some domains
Task 1
Task 1
Task 1
Task 4
Task 2
Task 3
36
Policy Refinement Policy Refinement
Goals:
Establish a layered policy model to reason about dynamic security policies.
Develop algorithms for refinement of policies between levels.
• A layered policy model has been formulated with four levels
– Specification, Abstract, Concrete, and Executable.
• At each layer
– policies need to be represented in a suitable notation
• Transformation procedures
– map between the policies between layers
Policy Specification
In Natural LanguageSubclasses (NLS)
In a Formal Language (FL)
System Side
Algorithms & Tools
User Side
Author NL policies
Convert NL policies to FL policies
Author FL policies
Convert FL policies to NL policies
Abstract Policy ModelsPrivacy / Security Ontologies
Policy Transformation
Policy Synchronization
Goals, High Level PoliciesIn System Context
Concrete Policy Sets
Executable Policies
Information Control Flow
Policy Ratification
Policy Authoring
Policy Ratification
Databases, XML Stores, Rule Engines, State Machines, etc
Large Scale Analyses of NL and FL Policies
Survey & Coding of Related Practices
Policy Transformation
Policy Synchronization
Human Factors Based Design & Usability Studies
Policy Presentation
Processing & User Interaction
User Preferences in a FL
User-Level Paradigms
for Preferences
Preference Specification Tools
AC & Audit Policies Data User Risk Choices & Model Model Model Consent
37
Theoretical Foundations for Policy Specification and Analysis
Theoretical Foundations for Policy Specification and Analysis
• Analyze Policies at different levels– Algorithms for feasibility and analysis– Determination of deployability and enforceability– Applicability analysis – Conflict removal and negotiation across domains
1. User Intuitive notation
Feedback on feasibility
2. Safety, liveness goals
3. Abstract state machine
4. Concrete state machine
5. System and concrete policies
compilation
analysis
enforceability
refinement
6. Policy negotiation
38
Policies to Enable Secure Dynamic Community Establishment Policies to Enable Secure Dynamic Community Establishment
• Objective
– define policy-based algorithms for establishing communities of mobile entities
• Develop Algorithms for
– policy deployment in response to changing conditions in dynamic communities
– revocation of non-relevant or unsafe policies.
– Discovery, authentication and role-assignment of network elements
– Self-management and self-protection
– negotiation of trust relationships
39
Distributed Policy Enforcement for Secure Information Flows Distributed Policy Enforcement for Secure Information Flows
Develop dynamic, distributed security mechanisms for information flows.
Adapt network structures to optimize information dissemination.
• Schemes for optimizing flows based upon the filtering requirements of end nodes in the system.
• Aggregation of secure flows to minimize transmissions
• Development of an abstract policy algebra for distributing security enforcement throughout a MANET.
• Exploit Policy Algebra for deployment and enforcement
Outside
A
B C
Let C(Pi) be the cost of a policy being installed at node i.Switch configuration if C(PA) < C(PB) + C(PC)
Let R(Pi) be the risk function of Policy P at node i. If R(Pi) > L, reject policy deployment
40
IBM Research
City University of New York
Roke Manor Research Ltd.
Royal Holloway University of London
Project 5Project 5
Energy Constrained Security Mechanisms for MANETs
Champion: Kenny Paterson, Royal Holloway
University of Maryland
University of York
41
Project 5 TeamProject 5 Team
• US– Academia
• Jonathan Katz, UMD • Kent Boklan, CUNY
– Industry• Pankaj Rohatagi, Tal Rabin et. al. IBM
– Government• Richard Gopaul, ARL
• UK– Academia
• Kenny Paterson, Stephen Wolthusen, RHUL • John Mcdermid, John Clark, John Murdoch, York
– GovernmentHelen Phillips, Dstl
42
Project 5 OverviewProject 5 Overview
• Goal– Enable security for information flows in flexible dynamic coalitions with multiple communities,
dynamic node mobility and constrained power. • Pioneer use of new security infrastructures, key management techniques and lightweight security
mechanisms/protocols in dynamic, mobile, ad hoc military networking environments• Understand interactions between security and heterogeneity in military networking environments• make security an enabler rather than a hindrance for collaboration in dynamic CoIs
• US/UK Collaborations– RHUL, UMD and IBM US working together on applying threshold cryptography to MANETs.
• Key Achievements for 2006– Developed techniques for inter-operation of entities with different trust authorities to enable
dynamic coalition formation. – Developed usage models and scenarios for Identity based keying in MANET environments.
• 2007-2008 Objectives– Threshold approaches to building security services in MANETs – Lightweight security infrastructures for MANETs– Mechanisms enabling secure information flows
• Military Relevance– Efficient security protocols for better efficiency of coalition networks
43
Threshold Cryptography for Survivable MANET InfrastructuresThreshold Cryptography for Survivable MANET Infrastructures
• What are the cryptographic solutions for complex situations which have– Low interaction and low computation – Adaptive thresholds– Require graceful degradation with number of compromised nodes– Distributed key management with provable signing properties
PK
Network Certificate
44
Lightweight Security Infrastructures for MANETs
• Goal: – Investigate alternative security
infrastructures for MANETs• Approach using ID-PKC and CL-PKC
– Public keys derived directly from system identities (e.g. an IP address).
– Private keys generated and distributed to users by a Trusted Authority (TA) using a master key.
– Allows encryption without certificates or directories
TA
Secure channel
Authentic public parameters
Alice’s ID
• Challenges– How to perform Namespace/identifier selection for scalability
and interoperability– How to develop distributed trust authorities
45
Secure Information FlowSecure Information Flow
Goal: Develop mechanisms for secure information flows in MANETsUnderstand trade-off between availability and protection in presence of compromised nodes:
• Challenges:– What is the right security metadata semantics for MANETS– How can one handle uncertainty in labels and data transformation– What are the efficient metadata transmission methods.– How does one detect and react to breaches in metadata integrity
Wired or Satellite Infrastructure
X
Info Flow
46
IBM Research
Cranfield University
Royal Holloway University of London
Project 6Project 6
Trust and Risk Management for MANETs
Champion: John Clark, York
University of Maryland
University of York
47
Project 6 TeamProject 6 Team
• US– Academia
• Virgil Gligor, UMD – Industry
• Dakshi Agrawal, Josyula Rao et. al. IBM– Government
• Natalie Ivanic, ARL • UK
– Academia• Kenny Paterson, Shane Balfe, RHUL • John Mcdermid, John Clark, John Murdoch, York• Howard Chivers, Cranfield University
– GovernmentOlwen Wirthington, Dstl
48
Project 6 OverviewProject 6 Overview
• Goal– Incorporate the concept of acceptable trust in coalition operations to
make security and enabler of coalition operations, as opposed to a hinderance.
• US/UK Collaborations– York, RHUL and IBM US working together on development of trust and
risk calculus.
• Key Achievements for 2006– Developed techniques for fuzzy logic based risk calculation and access
control.
• 2007-2008 Objectives– Dynamic Distributed Risk Estimation in MANETs – Risk Calculations
• Military Relevance– Enable an understanding of trust and risk trade-offs in coalition
operations
49
Risk and Trust Estimation in MANETs: Technical Approach
Risk and Trust Estimation in MANETs: Technical Approach
• Goals–Define trust that is usable in MANETs
• Advanced probabilistic mechanisms for computing trust
• Algebra for composing trust in an adhoc network
– Develop Feedback mechanism/learning algorithms for adjusting recommendation weights
• Subactivities – Develop formal adversary models
• models of adversary behavior are a crucial factor in determining risk.
– Develop trust calculus and logic• Provides a way to combine and
computer metrics for trust – Develop boot-strapping protocols
and mechanisms• How does one establish trust in the
beginning• How can initialization steps be
simplified and more efficient. Initial
Bootstrapping
AdversaryModel
AdversaryModel AdversaryModels
TrustAlgebra
To trust or not to trust? That is the
question
Trust level is high
Armed PersonApproaching
50
Risk Information, Policy & Decision SupportRisk Information, Policy & Decision Support
• Goal: Algorithms and Mechanisms to– Determine factors affecting risk decisions– Determine information needed for risk decisions– Handle lack of required information– Formulate risk policy formulation and control its evolution– Express risk data to make it usable
• SubActivities– Extension of fuzzy logic risk calculation mechanisms to uncertain
environments and risk modulating factors.– Automatic Inference of risk policy and its evolution with
additional factors– Presentation of risk – How to incorporate factors of timeliness,
operational effectiveness and security exposure when summarizing risk of an activity
51
Risk CalculationRisk Calculation
• Estimated Risk Estimated Loss (disclosure)
Estimated Probability (disclosure)
2121
2
Prob
,1,0
,1,01
PPPPe) (Disclosur
CategoriesSubjectCategoriesObjectotherwiseP
LevelSubjectLevelObjectotherwiseP
MLS COMPUTES PROBABILITY AS BINARY OUR NEW APPROACH COMPUTES NON-BINARY PROBABILITY
COMPUTES non-binary P1 based on object level (value) and subject level (trustworthiness)
COMPUTES non-binary P2 based on fuzzy subject and object category membership.
BINARY: SUCCUMBING TO TEMPTATION
BINARY: INADVERTENT DISCLOSURE RISKVS. POTENTIAL JOB REQUIREMENTS
NON BINARY TRADEOFF: INADVERTENT DISCLOSURE RISK VS. JOB REQUIREMENTS
SUCCUMBING TO TEMPTATION
52
Honeywell Aerospace Electronic Systems
University of California Los Angeles
Imperial College, London
Project 7Project 7
Quality of Information of Sensor Data
Champion: Chatschik Bisdikian, IBM
University of Maryland
IBM Research
CUNY
53
Project 7 TeamProject 7 Team
• US– Academia
• Mani Srivastava, UCLA• Ping Ji, CUNY • Anthony Ephremides, UMD
– Industry• Chatschik Bisdikian, Pankaj Rohatagi, IBM• Vicaraj Thomas, Jim Richardson, Yunjung Yi, Honeywell
– Government• Tien Pham, ARL
• UK– Academia
• Duncan Gillies, Erol Gelenbe, Imperial
– GovernmentRobert Young, Dstl
54
Project 7 OverviewProject 7 Overview
• Goal– Develop technologies to describe, analyze and estimate the quality of information delivered
by a sensor network. • How good is the sensor information and how is it affected by network and sensor characteristics
• US/UK Collaborations– Imperial and Honeywell working together on development of Impact of routing and energy on
QoI.– Imperial and IBM working together on QoI Calculus
• Key Achievements for 2006– Developed statistical and physical model techniques for in-network blind calibration. – Analysis of relationship between QoI and Sensor Sampling Policies– Impact of routing on timeliness of information.
• 2007-2008 Objectives– QoI Specification and Analysis Framework – Sensor characteristics and QoI – QoI and Network Services– QoI Calculus for Event Detection
• Military Relevance– Improvements in the quality of information delivered by the sensor network infrastructure
55
QoI Specification and Analysis FrameworkQoI Specification and Analysis Framework
• Goal:– A formalization of QoI that
allows applications to query and reason about sensors, information processing functions and information flows in terms of their QoI attributes.
• SubActivities– Application Selection – Application Formalization – Application
Characterization– QoI Abstraction and
Characteristics
Pro
venan
ce
QoI
Tim
elines
s Accu
racy In
tegrit
y
56
Sensor Characteristics and QoISensor Characteristics and QoI
• Goal: To understand– What factors affect the integrity of
sensor measurements?– How can we formally model their
impact?– What is the impact of integrity of an
individual sensor on the eventual post-fusion QoI?
– How to detect failure of sensor integrity?
– How to be resilient to such sensor integrity failure?
• Approach– Modeling Sensor misbehavior – Multivariate Analysis to detect incipient
sensor faults– Model based data cleaning– Sensor Data Querying with in-network
fault detection and diagnosis– Understanding Integrity of sensor
Information
Contextual or multiscale information
Another modality on the same node
Nodes of Same Altitude or Depth
Proximate Nodes
Measurements at same time previous day
Recent Measurements
57
QoI and Network ServicesQoI and Network Services
• Goals– How to model impact of network behavior on QoI?
• Subactivities– How to exploit sensor proximity information to improve QoI?– How to design congestion control protocols that provide a given level of
QoI – Source quenching mechanisms to ensure a degree of redundancy in
networks.– How to obtain time-synchornization and localization in sensor networks
Primal dataflows/streams
(raw data)
Aggregated dataflows/streams
Derived dataflows/streams
……
SensorNetwork A
(dataType_A)
SensorNetwork N
(dataType_N)
Data collection “layer” Aggregation layer
aggregationpoint A
aggregationpoint N
…………
Inference layer
Derived highlevel knowledge
…
…
……
Decisionmaker(s)
58
QoI Calculus for Event DetectionQoI Calculus for Event Detection
• Goal:– Determining QoI from event detection
information • Approach
– Hypothesis Testing based on fusion of several event signatures
• Which event hypothesis is true and map it to accuracy.
– Determine sampling rate to accurately detect event signature
• How less frequent than Nyquist frequency can one go.
eventcharacteristics
eventcharacteristics
sensor operationa
lcharacteris
tics
sensor operationa
lcharacteris
tics
QoIQoI
0.00
0.20
0.40
0.60
0.80
1.00
1.20
time, t
s(t
)
signal
samplemeasurements
sensormodule
(sampler)
fusionmodule
signal s(t)
decide whether event E has occurredbased on the measured signal samples
E?
connectivity
noise n(t)
1{ , , }nr r1{ , , }ns s
1{ , , }n Tt t W
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.8 1.6 2.4 3.2 4 4.8 5.6( / )h
DP
110a
310a
510a 0.50
2
T
n
0.50
2
T
n
fast-decayingsignal
slow-decaying signal
59
IBM Research
Pennsylvania State University
IBM UK
University of Aberdeen
3
4
8
Project 8Project 8
Task Oriented Deployment of Sensor Infrastructure
Champion: Thomas La Porta, Penn State University
City University of New York
Imperial College, London
60
Project 8 TeamProject 8 Team
• US– Academia
• Thomas La Porta, Penn State• Ted Brown, Amotz Bar-Noy, CUNY
– Industry• Archan Misra, IBM
– Government• Raju Damarla, ARL
• UK– Academia
• Alun Preece, Aberdeen• Kin Leung, Imperial
– Industry • Andy Stanford-Clark et. al., IBM
– GovernmentStuart Colley, Dstl
61
Project 8 OverviewProject 8 Overview
• Goal– Develop technologies to capture mission requirements and to configure, provision
and optimize sensor information fusion infrastructure to best support the mission requirements.
• US/UK Collaborations– IBM UK and IBM US working on applying message fabric infrastructure to sensor
networks.
• Key Achievements for 2006– Developed algorithms for optimal assignment of sensors to missions – Pioneered use of message queue infrastructure for sensor information
processing.
• 2007-2008 Objectives– Sensor Mission Matching– Mission Specific Network Configuration– Direction and Dissemination
• Military Relevance– Optimal use of resources to get “best” and most important intelligence in a timely
manner to the right parties
62
Sensor Mission MatchingSensor Mission Matching
Find an assignment of sensors to missions that (i) maximizes number of satisfied missions (max
number)(ii) maximizes number of missions according to
strict priority(iii) maximizes the sum of demands of satisfied
missions (max utility)(iv) maximizes the sum of values of satisfied
missions (max utility)(v) minimizes the sum of demands minus the utility
of assigned sensors (partial satisfaction)
(ongoing)
Sensor field at rest
- Low priority event
Sensor field at rest
- High priority event
Sensor field at rest
63
Sensor Mission MatchingSensor Mission Matching
Approach• Define mission representation
– Starting point: US and UK approaches, for which we have access to documentation
• Define a formal Sensor Ontology in a standard representation language (e.g. OWL)
• Define algorithms and protocols to optimally assign sensors to mission
– Semantic reasoner to determine “best” data sources
– Distributed algorithms to assign deployed sensors
MatchingPhase I: Semantic Matching (Fitting)• Select best set of sensor for a mission
Phase II: assigning specific sensors• Consider case where one sensor can service
more than one mission (simultaneous or TDMA)• Consider multi-modal sensors • Consider incremental sequence of missions,
where there is a cost for switching form the current assignment
• Consider the delay/cost of implementing a distributed solution
Mission
OperationOperation
TaskTask
Task
Component
System
Platform
CapabilityCapability
Capability
Capability requirements to perform tasks to standard
under given conditions
64
Mission Specific Network Configuration (joint with TA1)
Mission Specific Network Configuration (joint with TA1)
Problem statement: consider all aspects of mission-specific network configuration, from initial node deployment to network configuration to sensor output adaptation, that require dynamic adaptation over the lifetime of multiple, competing, concurrent missions
Approach:• Initial node deployment
– geometric considerations of initial node placement considering both sensing and communication
– consider realistic environments with non-accurate placement, obstacles, etc.
• Apply Network Utility Maximization (NUM) framework– Provides dynamic tuning of sources– Sources set rates to maximize their utility based on a “price” the network charges– Routers set prices based on their current level of congestion Must be adapted to WSNs
• Network reconfiguration– Enable new nodes (sensors) and links to accommodate traffic– Consider bandwidth (interference) limitations
65
Direction and DisseminationDirection and Dissemination
Problem statement:
Data must be delivered efficiently to those that need it (e.g., soldiers, analysts, other sensors) within a time constraint in the face of varying network conditions and mobility
Approach– Information Filters
• Only necessary information is sent to consumers• Achieved by user feedback and machine learning• Modularized according to roles on a team• Real-time, time varying information filters
–Eliminate information that is not useful given delivery constraints
– Define temporal and priority relationships between data items • mission and consumer specific
– Delivery schedule• Consider mobility of consumers and sources, varying network bandwidth, intermittent connectivity• Local dissemination protocols
–Used for passing network state information and local observations between sensors
• Push schedules for direction–Provide missions descriptions and assignments to sensors
66
IBM Research
City University of New York
IBM UK
University of Aberdeen4
Project 9Project 9
Complexity Management of Sensor Data Infrastructure
Champion: Boleslaw Szymanski, RPI
Rensselaer Polytechnic Institute
University of Southampton
67
Project 9 TeamProject 9 Team
• US– Academia
• Boleslaw Szymanski, UCLA• Abbe Moskowitz, CUNY
– Industry• Mandis Beigi, Dinesh Verma, IBM
– Government• Lance Kaplan, ARL
• UK– Academia
• Alun Preece, Aberdeen• Mark Nixon, Southampton
– Industry • Graham Bent et. al., IBM
– GovernmentMatt Brown, Dstl
68
Project 9 OverviewProject 9 Overview
• Goal– Develop technologies to capture mission requirements and to configure,
provision and optimize sensor information fusion infrastructure to best support the mission requirements.
• Key Achievements for 2006– Developed paradigm for sensor as a distributed network database – Development of opportunistic routing mechanisms for sensor networks.
• 2007-2008 Objectives– User Oriented Information Processing and Retrieval Paradigms– Semantically Mediated Data Fusion– Root Cause Analysis and Overload Protection
• Military Relevance– Simplify the management and interpretation of sensor information by the
warfighter during tactical operations.
69
User Oriented Information Processing and Retrieval ParadigmsUser Oriented Information Processing and Retrieval Paradigms
• Goal: – To provide a comprehensive
approach to the full spectrum of interactions of commanders and analysts with the sensor networks and their data
• Sub activities:– Service Oriented Architecture for
Sensor Networks: • to enable an easy integration of
sensor data with other sources data sources
– Sensor Networks as a Distributed Database
• to extends well-known and well-understood paradigm to sensor data retrieval.
– Market-based sensor network tasking:
• facilitates distributed assignment of sensor networks to tasks by coalition commanders optimizes the current set of executed missions
Service Composition
Optimized Deployment
Component Discovery
Mission Tasking
Process Choreography
Tactical
Info
rmatio
nS
enso
r Taskin
g
Integ
ration
(En
terprise S
ervice Bu
s)
Qo
S L
ayer (Secu
rity, Man
agem
ent &
Mo
nito
ring
Infrastru
cture S
ervices)
Data A
rchitectu
re (meta-d
ata) & B
usin
ess Intellig
ence
Go
vernan
ce
Sensors
Systems domain
Network domain
C C C C C C C
CMCCentral Mission ControlCentral Banking Authority
Budget AllocationsMapping of MissionsInto Budget Decisions
Mission Commanders
Bids for services
Allocation Decisions
Bidding Strategies
AllocationPolicies
Sensor Networks
SN
SN SN
SN
SN
SN
SN
70
Semantically Mediated Data FusionSemantically Mediated Data Fusion
• Goal: – Extend data fusion by
incorporating trust and uncertainly in a semantically-mediated framework
• Approach 1. Trust Modelling
• Yu and Singh• FIRE• Travos
2. Data Fusion• Bayesian (probabilistic) methods• Evidential methods• Rough sets and fuzzy methods
3. Feature Set Selection• Algorithmic methods• Statistical approaches
4. Ontologies• to capture semantic information
Ontologies
Information Sources
TrustCertaintyContext
Classifi-cations
Fusion Processes
Feature extraction
Feature analysis
?
Measure 1
Measure 2
Two different classes
Decision boundary
Feature space
After semantically-enhanced fusion
After one sensor compromisedInitial position, two sensors
poor sensor
good sensor
71
Root Cause Analysis & Overload ProtectionRoot Cause Analysis & Overload Protection
• Goal– Move sensor information processing to higher levels of interpretation – Reduce the information overload on the user of the sensor network
• Approach– Apply root cause analysis techniques from systems management domain
to sensor information fusion– Develop an algebra to determine the mapping between sensor monitored
information and events.– Methods to refine and reduce sensor information stream based on
human perceived event
Sensor
Measurements
M1
M2
Event
Processing