1 us/uk international technology alliance (ita) john gowens arl collaborative alliance manager jack...

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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


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)


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.


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.


LethalSlow-growth

Non-lethalUnknown

Small Hop Count Wireless Network

28

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

29



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


Layer

Abstract PolicyLayer

Concrete PolicyLayer

Implementation and Configuration

Layer

Real-time Updates

30

Columbia University

Imperial College

University of Cambridge

Project 4Project 4

Policy based Security Management

Champion: Seraphin Calo, IBM

Honeywell Aerospace Electronic SystemsIBM Research

31


• 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

32


• 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


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


Policy System Translates

Operational PoliciesInto Machine-Readable


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


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


• 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


• 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 York

47


• 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


• 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


Champion: Chatschik Bisdikian, IBM


IBM Research

CUNY

53


• 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


• 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


Imperial College, London

60


• 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


• 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


- High priority event


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


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


• 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


• 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

http://www.fluent.com/solutions/examples/img/x209i1_lg.gif

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

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