opportunistic resource utilization networks (oppnets) for uav ad-hoc networking phase i final review...

Opportunistic Resource Utilization Networks (Oppnets) for UAV Ad-Hoc Networking

Phase I Final ReviewInfoscitex Corporation

25 Feb 2011

04/18/23 2

Agenda

• Project Team

• Technical Overview

• Task Summary and Discussion

• Future Work

• Conclusions

• Infoscitex Background

04/18/23 3

Project Team

• Infoscitex Corporation

– Principal Investigator: Andrew DeCarlo

Email: [email protected] / Phone: (781) 890-1338 x289

– Project Manager: Dr. Sherman Tyler

Email: [email protected] / Phone: (781) 890-1338 x263

• Subcontractors

– Western Michigan University: Dr. Leszek Lilien

– Purdue University: Dr. Bharat Bhargava

04/18/23 4

Agenda

• Project Team



• Future Work

• Conclusions


04/18/23 5

Problem to be Solved

• Resource virtualization maximizes distributed application performance– Resources allocated and adapted on-the-fly– Allows a broad range of distributed computing, networking, and sensing

applications• Content- and context-based data management• Service-Oriented Architecture (SOA)• Virtual Private Networks (VPNs)• Coordinated network security

• Barriers to resource virtualization in mobile ad-hoc networks (MANETs)– MANETs are less structured than traditional networks– Special challenges result from this lack of structure:

• Frequent link breakage• Inconsistent data rate• Incompatibility of resources• Temporary unavailability of needed resources and communication links

04/18/23 6

The Infoscitex Solution (Oppnets)

• Opportunistic Resource Utilization Networks (Oppnets) for UAV Ad-Hoc Networking:– Novel MANET consisting of

an initial seed network that temporarily recruits resources.

– Oppnets:• Allow the construction of highly

adaptive, flexible, and maintainable application networks

• Utilize and enhance applications, even including inflexible, stovepiped, legacy applications

• Adapt and optimize the use of resources on-the-fly

• Enable and facilitate distributed applications

• Virtualize resources across platforms, allow scalability, and promote dynamic growth

– Oppnets are:• Opportunistic resource/capability

utilization networks• Opportunistic growth networks• Specialized Ad-Hoc

Networks/Systems (SAHNS)

– Oppnets are not:• “Generic” ad-hoc networks• Mesh networks• Grid computing systems• P2P networks• Opportunistic connectivity networks

Oppnets exploit diverse capabilities such as radio spectrum, connectivity, computing power, sensing, actuation, and image recognition

04/18/23 7

The Oppnet Concept

Oppnets recruit and coordinate the capabilities of diverse networks, sensors, and computational resources in a way that optimizes resource utilization and also ensures improved QoS despite intermittent link connectivity.

Satellites

Radar Processing

CarrierMerchant Ships

USVs

LCSs

Underwater Acoustic

Array

Target

Fighter

X-47B UCASSeed Oppnet

Oppnet links non-Oppnet UCAS links to Carrier

04/18/23 8

Technical Objectives

• Identifying Key Use Cases:– Identify one or two basic use cases for proof of concept, including any:

• Mobility models• Helper networks• Necessary resources

– Develop tactical Oppnets based on use cases

• Developing Tactical Oppnet Capabilities:– Implement resource virtualization, network optimization, and network expansion

capabilities within scope of use cases– Emphasize security, modularity, scalability, SOA support, and QoS improvement– Tailor Oppnets for X-47B UCAS and other platforms

• Testing and Demonstrating Oppnets:– Simulate Oppnets’ performance in software– Fine-tune the general Oppnet implementation for selected use cases– Hardware testbed simulation – Proof-of-concept demonstration

04/18/23 9

Agenda

• Project Team



• Future Work

• Conclusions


04/18/23 10

Phase I Milestone Schedule

Base Mo. Option Mo.Task 1 2 3 4 5 6 7 O1 O2 O3

Milestone Kickoff Meeting

Task 1 Identify Key Use CasesTask 2 Develop Tactical Oppnet CapabilitiesTask 3 Test and Demonstrate OppnetsTask 4 Program Management and Reporting

Task O1 Prepare for Phase II

Milestone Interim Status ReportsMilestone Final Review with DemoMilestone Phase I Draft Final ReportMilestone Phase I Final Report

04/18/23 11

Task 1: Identify Key Use Cases

• Use Case Features: – Carrier Strike Group (CSG) consisting of carriers, Littoral

Combat Ships (LCSs), and other air/surface vehicles– X-47B UCAS on carrier acts as a seed Oppnet– Seed recruits capabilities including:

• Sensing• Data links• Computation• Actuation

– Resource/capability virtualization methods include:• Service directory lookup• Lookup from helper networks’ service directories• True discovery

Oppnet Use Case Example

Satellites

Radar Processing

CarrierMerchant Ships

USVs

LCSs

Underwater Acoustic

Array

Target

Fighter

X-47B UCASSeed Oppnet

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Task 1: Identify Key Use Cases

• Helpers 4-7 used to compute statistics in simulation results: – The seed Oppnet needs to integrate a radar plot, and requests assistance from

LCS1 (Helper 4).– Helper 4 is unable to do the integration itself, so it recruits the satellite link

(Helper 5) to search for available services.– Helper 5 connects to several different radar processing capabilities (across two

hops total) that comprise Helper 6.– The integrated radar plot returned by Helper 6 comes up empty, so the seed

Oppnet truly discovers an F/A-18E fighter flying overhead. The F/A-18E becomes Helper 7, and identifies and localizes the target, allowing the seed Oppnet to send pursuit vehicles after the target.

• Helpers 4-7 are key to the use case– Involve scalability, multiple hops, and resource/capability virtualization (Helper 6)– Use all three discovery types (service directory lookup, discovery through helper’s

service directory, true discovery)– Improve the UCAS’ speed and accuracy in identifying and apprehending a fast-

moving surface target

Use Case Breakdown

Radar Plot AnalyzerHelper 6

UCASSeed Oppnet

Radar Plot IntegratorHelper 4

AEHF SatellitesHelper 5

36a

F/A-18E Super Hornet,Helper 7

46

18

26

2447

19

29

23

17

20

21 22

2527

28

30

31

32

33

3435

36b

37

38

39

40

414243

44

45

48

49

50

51

52

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Task 2: Develop Tactical Oppnet Capabilities

• Oppnet Capabilities– Resource/capability virtualization, network optimization,

network expansion– Capabilities are implemented with an emphasis on:

• Security• Modularity• Scalability• SOA support• QoS improvement

– Previously demonstrated in CBRN first-responder applications

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• Lookup Subsequence1) look up directory and identify reservist

helper2) order to join3) joins and is integrated into Oppnet

[Note: We assume for now that all ordered helpers are able to join.]

4) order helper to provide (activity) report OR: order forwarding a task/message to

helper H and for H's (activity) report 5) helper does its job6) helper sends result report7) receive report from helper OR: receive and forward report8) release helper (i.e., sends the release msg

to the helper).

• Discovery Subsequence0) failed look up for reservist helper1) attempt discovery: scan & discovery

(are discovered non-reservists Oppnet-enabled or not?)

2) ask to join3) agree to join or not; if agreed, joins and

is integrated into Oppnet4) ask helper for (activity) report OR: ask for forwarding a task/message to

helper H and for H's (activity) report5) helper does its job6) helper sends result report7) receive report from helper OR: receive and forward report8) release helper

Sequence of Oppnet Operations

• Oppnet considerations:– Must not disrupt critical

operations

– Must perform risk evaluation

– Must assure privacy and security

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Oppnet Expansion Process

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Partial List of Oppnet Virtual Machine Primitives

Name Functions

CTRL_start Initiate Oppnet

CTRL_end Terminate Oppnet

CTRL_cmd Send commend to seed nodes

Report information to control center/coordinatorSEED_report

Process a message from bufferSEED_processMsg

Release a helper when no longer neededSEED_release

Delegate a task that requires a permission from the delegating entity

SEED_delegateTask

Send a task to other Oppnet deviceSEED_sendTask

Evaluate a device and admit it into Oppnet if the device meets criteria for admittance

SEED_evalAdmit

Checks if a device is already an Oppnet node (Oppnet member)

SEED_isMember

Verify the received commandSEED_validate

Receive and save messages in buffer SEED_listen

Discover candidate helpers with a specific communication mechanism

SEED_discover

Scan communication spectrum to detect devices that could become candidate helpers

SEED_scan

FunctionsName

Seed NodesCC Nodes

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Partial List of Primitives for Helper Nodes

Name of the Primitive Functions of the Primitive

HLPR_isMember Test if a helper is already a member of oppnet

HLPR_joinOppnet Join oppnet

HLPR_scan Scan communication spectrum to detect devices that couldbecome candidate helpers (regular or lites)

HLPR_discover Discover candidate helpers with a specified communicationmechanism

HLPR_validate Validate the received command

HLPR_switchMode Switch between helpers’ regular application and oppnet application

HLPR_report Send information/data to specified device

HLPR_selectTask Select a task from the task queue to execute

HLPR_listen Receive message and save it

HLPR_evaluateAdmit Evaluate a candidate helper and admit it into oppnet if it meets criteria defined by oppnet

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Partial List of Primitives for Helper Nodes (cont.)


HLPR_runApplication Execute application indicated by authorized oppnet seed or helper node

HLPR_release Release a helper (unless delegated a release task, a helper H can release only helpers admitted by H)

HLPR_processMsg Process a message from buffer

HLPR_sendData Send information/data to specified authorized oppnet node

HLPR_leave Inform a seed that the caller will quit oppnet

HLPR_strongTask Respond to the request sent from device and express the willingness to join oppnet. By accepting this task, the device will abort previous task

HLPR_weakTask Respond to the request sent from device and express the willingness to join oppnet. By accepting this task, the device will put the task in a queue

HLPR_assignStrongTask

Assign tasks to a device. If accepted, the task will interrupt the previous task at the device

HLPR_assignWeakTask Assign tasks to a device. If accepted, the task will be queued

04/18/23 21

Partial List of Helpers for Lightweight Nodes


LITE_isMember Test if a lit is already a member of oppnet

LITE_joinOppnet Join oppnet

LITE_validate Verify the received command

LITE_switchMode Switch between lites’ regular application and oppnet application

LITE_report Send information/data to specified device

LITE_selectTask Select a task from the task queue to execute

LITE_listen Receive message and save it

LITE_runApplication Execute application indicated by authorized oppnet seed or helper node

LITE_processMsg Process a message from buffer

LITE_sendData Send information/data to specified authorized oppnet node

LITE_leave Inform a seed that the caller will quit oppnet

LITE_strongTask Respond to the request sent from device and express the willingness to join oppnet. By accepting this task, the device will abort previous task

LITE_weakTask Respond to the request sent from device and express the willingness to join oppnet. By accepting this task, the device will put the task in a queue

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• Oppnets as an extension of SOA– SOA limited to lookup via predefined service directories in infrastructure– Oppnets also provide true discovery

• QoS in Oppnets– Common QoS requirements include:

• Availability• Accessibility• Integrity • Performance• Reliability

– Seed Oppnet itself might not possess all capabilities necessary to meet QoS requirements• Pre-registered Reservists will provide the needed capabilities• Other (discovered) helpers may improve QoS further• Oppnets must invoke and utilize all capabilities in network to meet user-defined QoS

requirements (e.g., time-sensitivity)– Semantic Web capabilities– QoS requirements may also assist in helper discovery and selection

04/18/23 24

Task 3: Test and Demonstrate Oppnets

• Software Simulation– Fine-tuning Oppnet implementation– Providing information for customizing the

implementation per each use case– Demonstrating feasibility

04/18/23 25

Task 3: Test and Demonstrate Oppnets

GOAL: Test the UCAS’ speed and accuracy in apprehending a fast-moving speedboat without (left) and with (right) Oppnets helpers

UCAS Speedboat

Ac. Array

UCAS SpeedboatF/A-18E

Ac. Array

04/18/23 26

Simulation Input Parameters Variable Value Description

PRNGseed 1000 The seed used for Pseudo Random Number Generator (PRNG)

AreaMaxX 100 Maximum value for the x coordinate defining AOR [miles]

AreaMaxY 100 Maximum value for the y coordinate defining AOR [miles]

UcasSpeed 300 Speed of the UCAS [mph]

UcasSensorRange 10 The radius for the circular range of the UCAS sensors [miles].

SpeedboatSpeed 90 Cruising speed of the speedboat in calm waters (80 knots= approx. 90 mph) [mph]

SuperHornetSpeed 777 Cruising speed of the F/A-18E [mph]

FighterSensorRange 20 The radius for the circular range of the F/A-18E sensors [miles].

OppnetDelayMin 3 Minimum value for the delay in integrating the F/A-18E helper by UCAS [minutes]

OppnetDelayMax 66, 33, 22,16,13

Set of maximum values for the delay in integrating the F/A-18E helper by UCAS [minutes]

ProbSpeedboatDetection 1[1] Probability that the speedboat will be detected by UCAS sensors and F/A-18E sensors if it is within their sensor range

[1] Values < 1 will be considered in future simulation runs.

04/18/23 27

Simulation Random Variables Random Variable

Value Range Statistical Value Distribution

Description

DetectedSpeedboatPosition

xval: 0 - AreaMaxX, yval: 0 - AreaMaxY

Uniform distribution The position where an Acoustic Array detects the speedboat is: (xval, yval).

FinalSpeedboatPosition

xval : 0 - AreaMaxX

Uniform distribution The final speedboat position is: (xval, 100)

InitialFA18ExPosition

xval : 0 - AreaMaxX

Uniform distribution The point (at the bottom of AOR) at which the F/A-18E enters the AOR[1] is: (xval, 0).

TimeToIntegrateFA18EhelperByUcas

tti: 3 – MaxTime, where MaxTime ϵ {66, 33, 22, 16, 13}

Uniform distribution This is time before UCAS can start using F/A-18E as a helper. It is the sum of the period before UCAS starts looking for F/A-18E[2] plus the period taken to find the F/A-18E helper and complete integrating it.

[1] By simulation assumption, the yval of the point at which the F/A-18E enters AOR is 0.[2] Before starting to look for F/A-18E as a helper, UCAS asked for help 4 other helpers. Time to ask these 4 helpers and to find out that another helper is needed is the sum of individual times needed for each of these 4 helpers. Each individual time includes time for UCAS to locate and integrate the helper plus time to send the UCAS’ help request message to the helper plus time needed by the helper to process the help request and reply UCAS, and time for the helper’s reply message to reach UCAS. Time for forwarding messages among these helpers must also be added.

Results: Varying Delay in Integrating Helper for Speedboat Detection (Delays and Success Ratios)

Range for Delay in

Integrating Helper

Success Ratio for

Seed Oppnet

Success Ratio for Extended Oppnet

Time till Seed Oppnet

Detects Speedboat

Time till Oppnet Completes Helper

Integration (for runs with successful speedboat

detection)

Time till Extended Oppnet

Detects Speedboat

Average Time

StandardDeviation

Average Time

StandardDeviation

Average Time

StandardDeviation

Range 1: [3-66]

27% 25% 33.73 9.66 15.46 5.94 18.75 6.16

Range 2: [3-33]

27% 49% 33.73 6.99 12.69 5.58 15.94 6.10

Range 3: [3-22]

27% 61% 33.73 6.99 10.24 4.29 13.25 4.72

Range 4: [3-16]

27% 75% 33.73 6.99 8.39 3.24 11.32 3.66

Range 5:[3-13]

27% 85% 33.73 6.99 7.44 2.82 10.37 3.33

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Varying Delay in Integrating Helpervs. Delay in Speedboat Detection

04/18/23 29

Integration Delay

Integration Delay

Varying Delay in Integrating Helpervs. Success Ratios without and with Helper

04/18/23 30

Integration Delay

Results: Varying Helper Density for Speedboat Detection (Delays and Success Ratios)

Number of Fighter

Helpers

Avg. forExtended Oppnet

Success Ratio

Std Dev. for Extended Oppnet

Success RatioAvg. for

(a)-(b) Std Dev. for

(a)-(b)

1 59.00% 23.41% 3.08 0.177

3 76.80% 22.91% 2.09 0.229

5 81.00% 19.72% 1.61 0.216

7 82.80% 19.69% 1.40 0.132

9 84.20% 18.07% 1.26 0.131

11 85.00% 17.36% 1.19 0.107

13 85.40% 16.94% 1.14 0.102

15 85.60% 16.67% 1.10 0.063

17 85.60% 16.50% 1.09 0.061

19 86.20% 16.24% 1.07 0.057

04/18/23 31

Varying Helper Density vs. SuccessRatios without and with Helper (Delay Range 1)

0%

10%

20%

30%

40%

50%

60%

70%

1 3 5 7 9 11 13 15 17 19

seed Oppnet successratio

Extended Oppnet successratio

Success ratio

Density of helpers

04/18/23 32

Varying Helper Density vs. SuccessRatios without and with Helper (Delay Ranges 2 and 5)

0%

20%

40%

60%

80%

100%

120%

1 3 5 7 9 11 13 15 17 19

Seed Oppnet successratio

Extended Oppnetsuccess ratio

Success ratio

Density of helpers

04/18/23 33

Varying Helper Density vs. Helper Integration Delay and Speedboat Detection Time (Delay Ranges 1 and 5)

0.00

5.00

10.00

15.00

20.00

25.00

30.00

1 3 5 7 9 11 13 15 17 19

Average time tillOppnet integrateshelperAverage time tillextended Oppnetdetects speedboat(a)-(b)

Time

Density of helpers

0.00

2.00

4.00

6.00

8.00

10.00

12.00

1 3 5 7 9 11 13 15 17 19

Average time tillOppnet integrateshelperAverage time tillextended Oppnetdetects speedboat(a)-(b)

Time

Density of helpers

04/18/23 34

Single-Fighter Denial of Help (Delays and Success Ratios)

04/18/23 35

Range for Delay in Integrating Helper

Success Ratio for

Seed OppnetSuccess Ratio for Extended Oppnet

Success Ratio for Extended

Oppnet – denial of help with

probability 0.2



probability 0.6



probability 0.8

Range 1: [3-66] 27% 25% 20% 10% 5%

Range 2: [3-33] 27% 49% 39% 19% 9%

Range 3: [3-22] 27% 61% 51% 24% 11%

Range 4: [3-16] 27% 75% 60% 29% 13%

Range 5:[3-13] 27% 85% 66% 32% 15%

Single-Fighter Denial of Help (Delays and Success Ratios)

04/18/23 36

Multi-Fighter Denial of Help (Delays and Success Ratios)

04/18/23 37

Range for Delay in Integrating Helper

Average Time till Seed Oppnet Detects

Speedboat

Average Time till Extended

Oppnet Detects

Speedboat

Average Time till Extended Oppnet

Detects Speedboat –

denial of help with probability 0.2

Average Time till Extended Oppnet

Detects Speedboat – denial of help

with probability 0.6

Average Time till Extended

Oppnet Detects Speedboat – denial of help

with probability 0.8

Range 1: [3-66] 33.73 27.23 18.75 19.23 20.26

Range 2: [3-33] 33.73 22.38 15.57 15.27 16.41

Range 3: [3-22] 33.73 18.74 13.35 13.27 13.44

Range 4: [3-16] 33.73 16.75 11.51 11.48 11.55

Range 5:[3-13] 33.73 15.82 10.28 10.26 10.13

Multi-Fighter Denial of Help (Delay Range 1)

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04/18/23 40

Conclusions

• 10-helper use case broken down into 61-interaction simulation– Service directory lookup, helper directory lookup, and true discovery

have all been simulated.

– The effects of all relevant primitives have been simulated and verified with respect to the use case.

• True discovery proves to be a very beneficial asset because:– Truly-discovered helpers can detect the speedboat in <4 sec after

integration, compared with nearly 34 sec for directory-lookup helpers.

– Success rate is at least two times higher with a truly-discovered helper than without one

• Quickly approaches 100% for higher-helper-density lower-integration-delay scenarios.

04/18/23 41

04/18/23 42

Agenda

• Project Team




• Future Work

• Conclusions

Future Work

• Considered Future Extensions:– Denial of help: Demonstrate the effects of a helper being unable to help.– Less-invasive help mode: Allow helpers (including truly-discovered helpers) to

operate without requiring host/human intervention.– Introduce effects of detection probability: Vary the detection probability to

address different surface conditions.– Introduce sensor array coverage areas: Simulate marginal and certain

detection by acoustic sensor arrays.– Change initial speedboat position.– Vary speedboat movement patterns: Change from straight-line motion to

random changes in velocity (e.g., evasive actions).– Use more random variables for helper integration: Assign random variables to

quantify communication/processing among the helpers.– Consider longer helper integration delays.– Vary AOR size: Currently 100 mi by 100 mi.– Emphasize Radar Plot Integration: We currently assume radar plot integration

in Helper 6 always fails. Consider variable plot integration success/failure.

04/18/23 43

Future Work

• Considered Future Extensions (continued):– Effects of scalability on radar plot integration: Vary number and variety of

resources/capabilities that comprise Helper 6, and show how this affects radar plot integration speed and success rate.

– Effects of service availability on radar plot integration: Vary whether or not resources/capabilities within Helper 6 are available.

– Merging Protocol Stacks: Show how merging protocol stacks within Helper 6 affects radar plot integration speed and success rate.

– Quality of Service: Measure the quality of service (available bandwidth, bottleneck bandwidth, one-way delay, packet loss ratio, etc.) in the communications links

– Model strength of assigned task: Assign strong tasks that require interruption of current tasks.

– Other extensions TBD

04/18/23 44

Phase II Task Plan

• Task 1: Collect User and Operational Requirements– Detailed analyses of mobility constraints (e.g., maximum speed, cruising speeds, aircraft service

ceiling), sensor parameters (e.g., range, field of view, sweep rate), etc. of identified helpers and SNAP entities

– Study of resources and capabilities desired by the SNAP end users

• Task 2: Implement Control/Seed Oppnet Virtual Machine (OVM) Primitives– Implement control and seed OVM primitives in the Oppnets testbed– Verify and validate primitives’ performance based on scalability, range of available

services/capabilities/resources, and speed/computational efficiency in carrying out a mission

• Task 3: Implement Helper OVM Primitives– Downselect to a controlled, well-defined set of helpers – Implement helper OVM primitives in the Oppnets testbed– Verify and validate primitives’ performance based on scalability, range of available

services/capabilities/resources, and speed/computational efficiency in carrying out a missio

• Task 4: Implement Lite OVM Primitives– Downselect to a controlled, well-defined set of lightweight nodes (lites)– Implement lite OVM primitives in the Oppnets testbed– Verify and validate primitives’ performance based on scalability, range of available

services/capabilities/resources, and speed/computational efficiency in carrying out a mission

04/18/23 45

Phase II Task Plan (cont.)• Task 5: Module Assembly and Debug

– Integrate the control, seed, helper, and lite OVM primitives as a system within the Oppnets testbed– Verify compatibility between modules– Develop any additional functionality and architecture requirements necessary for simulating the

primitives together on the Oppnets testbed

• Task 6: Simulation, Test, and Evaluation– Simulate the control, seed, helper, and lite OVM primitives as a system within the Oppnets testbed– Identify risks associated with further development – Develop a risk mitigation plan and list of design considerations

• Task 7: Further Research, Development, Test, and Evaluation (RDT&E)– Implement design improvements identified in Task 6– Perform additional system-level and module-level simulations as needed– Identify risks associated with large-scale systems integration

• Task 8: Systems Integration– Develop risk mitigation plan for large-scale systems integration– Begin integration of Oppnets with SNAP– Investigate other large-scale systems that can benefit from Oppnets in the short term

04/18/23 46

Phase II Milestone Schedule

MonthTask 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Milestone Kickoff MeetingTask 1 User/Operation RequirementsTask 2 Control/Seed OVM PrimitivesTask 3 Helper OVM PrimitivesTask 4 Lite OVM PrimitivesTask 5 Module Assembly and DebugTask 6 Simulation, Test, and EvaluationTask 7 Further RDT&ETask 8 Systems IntegrationTask 9 Project Management and Reporting

Milestone Interim Status ReportsMilestone Interim ReviewMilestone Final Review/DemonstrationMilestone Phase II Final Report

04/18/23 47

04/18/23 48

Agenda

• Project Team



• Conclusions


04/18/23 49

Agenda

• Project Team



• Future Work

• Conclusions


04/18/23 50

• Engineering, Research and Development– Develop advanced technologies

– Provide technical services

• Founded in 2000• Small Business

Who We Are

04/18/23 51

• Customer Mission Focused• Preeminent Technology Development• Employee Excellence & Gratification• Community Involvement• Commitment to Longevity & Prosperity

Corporate Vision

04/18/23 52

Corporate Timeline

Foster-Miller Founded

Systran Federal Founded

1956 1977 2000 2005 2006 2008

IST Founded

IST acquired Foster-Miller’s R&D Group

IST acquired Systran Federal

IST Energy Corporation Spun

Out

2009 Small Business of the Year by the Greater Boston Chamber of Commerce

Infoscitex Corporation Ranked No.1 Fastest Growing Private Company in New England 2008

04/18/23 53

Performance

04/18/23 54

Locations

Corporate Headquarters

Company Offices

External Facilities Agreements

04/18/23 55

• Advanced Composites • Artificial Organs • Biomaterials • Biomedical Sciences and Biomechanical

Engineering • Biotechnology • Business Process Reengineering & Web

Applications • Ceramics & Glass • Classified System Administration • Counterintelligence • Data Visualization • Decision Support • Embedded Controllers & Control Software

• Energetic Materials & Ordnance • High Performance Polymers • Human Factors Engineering • IED Defense

• Intelligence Analysis • Intelligence Collection Management • Minimally Invasive Technologies • Modeling and Simulation Software

Development • Nanotechnology • Quality Assurance • R&D Information Technology Support • Robotics, Mechanisms, & Electro-

mechanical Systems • Sensors & Data Acquisition • Signal Processing • Systems Protection • Tactical & Strategic Linguistics • Target Tracking • Thermal Management • User Interface Design • Weapon System Effectiveness • Wireless Communications

Capabilities

04/18/23 56

• Biological Sciences:– Biomedical Prototyping Lab– Microbiology Lab BSL2– Cell Culture Lab

• Physical and Material Sciences:– Acoustics Lab– Advanced Materials Lab– Composites Lab– Analytical Chemistry Lab– Chemical Processing Lab– Electro-Active Materials Lab

Laboratory Facilities

• Engineering and Electronics:– Electronics Lab– Machine Shop– Mechanical Test Lab– Flight Simulation– Modeling & Simulation Suites– ATF Type 33 License - User of High

Explosives

• Formal Outreach Relationships:– Air Force Research Labs Human

Effectiveness Directorate– Naval Surface Warfare Center (China

Lake)– Colorado State University BSL3

Facilities (in progress)

04/18/23 57

Customers– 3M– Birds Eye Foods– California Energy Commission– Celltech Pharmaceuticals– Choice One Communications– CooperVision– Corning Incorporated– Department of Commerce– Department of Defense– Department of Energy– Department of Transportation– Environmental Protection Agency– Excellus Health Plan– FedEx– Foster-Miller– Horizon Defense & Aerospace– MPower Communications

– MySky Communications– National Aeronautics & Space

Administration– National Institute of Health– National Science Foundation– New York State Electric & Gas– Ortho-Clinical Diagnostics– Reuters– Sage Research– Taconic– Trans World Entertainment– US Air Force– US Army– US Navy– US Marines– Valeo– Vibrant Solutions

04/18/23 58

Academic Collaborators (primary)

• Joint Strike Fighter Simulator, Boeing• B-2 Bomber Simulator, Boeing• C-130 Simulator, Raytheon• MSH Helicopter Simulator, CAE

Electronics Ltd.• V-22 Osprey Training Simulator, Flight

Safety• Aluminum Plant Rolling Mill, General

Electric• Autonomous Underwater Vehicle, Florida

Atlantic University• Ship Fire Control System, United Defense

Corp.• E2C Upgrade, Lockheed Martin

Sample Applications

Productization Partner

SCRAMNet-GT PCI

Example Product Success Story: SCRAMNet Technology

04/18/23 59

• F-18 Test Bench, Boeing

• Spacecraft Simulator, Honeywell

• Rocket Test Set, Lockheed

• E4B Test Lab, Boeing

• Robotic Welder, Lincoln Electric

• CNC Machine Control, SMS Group

• Pulp Refining, STEP Technology Inc.

• Machine Control System, Normac Inc.

• Wafer Inspection System, Torex Corp.

IPACK/PCI Carrier

Sample Applications

Productization Partners

Example Product Success Story: IPACK Technology

04/18/23 60

• Radar Test System, Aselsan A.S.• Reconfigurable Cockpit Simulator, Bell• 757 Remote Control Landing System,

NASA- Langley• Cockpit Display System Lab, Boeing-

Philadelphia• Telecom Test Lab, Alenia Aerospazio• Post Video Production, Warner Brothers • Towed Sonar Lab, Marconi Sonar• Torpedo Simulator, NUWC• THAAD Integration Lab, Raytheon• SAN Interoperability Test Lab, EMC

Corp.

Sample Applications


LX Switch Products

Example Product Success Story: LinkXchange Technology

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• B1 Development Lab, Northrop Grumman• F-16 Test Stand, USAF Hill AFB• Turkish Navy, Sikorsky• Global Hawk Lab, Northrop Grumman• B2 SIL, Raytheon• C-130 AMP, Boeing• CP140 Aurora Lab, General Dynamics• Apache Simulator, Camber• Space Shuttle Simulator, Space Alliance

Corp.

Sample Applications


1553 BusXchange

Example Product Success Story: MBS Technology

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Related Work

• Multi-Hop Base Station Mobility Management Scheme (MBSMMS)

• Sponsor: Army CERDEC• Objective: To allow mobile multi-hop base

stations in IEEE 802.16m networks– Mobility management protocols allow

seamless, efficient base station handoffs

– Multi-hop WiMAX networks allow beyond-line-of-sight (BLOS) communications with the bandwidth of the wired internet

– A novel, hierarchical security scheme prevents eavesdropping and spoofing attacks

• Complete: 2011• PI: Andrew DeCarlo

MMR-BS1 MMR-BS2

M-RS5

M-RS6

M-RS1 M-RS2

M-RS3 M-RS4

MS2

MS3 MS5 MS7

MS8

BS Cell

MS1

MS4

MS6

04/18/23 63

Related Work

• Adaptive Distributed Monitoring System (ADMS)

• Sponsor: AFRL Information Directorate

• Objective: To ensure high end-to-end performance in mobile ad-hoc networks

– Hierarchical, cluster-based monitoring approach

– Mobile agents roaming the network

– Ensures high quality-of-service (QoS) in a highly-dynamic MANET consisting of UAVs, manned aircraft, and ground stations

• Complete: 2008

• PI: Mike O’Connor

04/18/23 64

Related Work

• Secure Bulk-Transfer Mesh Network Protocols (MeshXPress)

• Sponsor: AFOSR

• Objective: To prototype an efficient, fair, and dynamic multi-path routing protocol

– Application-oriented protocol design dynamically balances the network load

– Queue management scheme mitigates distance-based unfairness

– Game-theoretical router selection further optimizes load balancing

• Complete: 2009

04/18/23 65

Related Work

• Wireless Network Denial-of-Service Distributed Monitoring System (WiNDoS-DOS)

• Sponsor: Army CERDEC• Objective: To develop a method of

regulating bursty flows while suppressing attack flows in wireless networks

– Throttles bursty flows to more manageable rates

– Prevents attack flows from entering the network

– Perceptron-based attack detector distinguishes between link congestion and DoS attacks

• Complete: 2008• PI: Andrew DeCarlo

04/18/23 66

opportunistic resource utilization networks (oppnets) for uav ad-hoc networking phase i final review...

Documents

networks manets manets

use of resources

tactical oppnets

carrier slide

computational resources

oppnet concept oppnets

communication links

dynamic growth oppnets