Opportunistic Resource Utilization Networks
(Oppnets) for UAV Ad-Hoc Networking
Phase I Final Review
Infoscitex Corporation
25 Feb 2011
Agenda
• Project Team
• Technical Overview
• Task Summary and Discussion
• Future Work
• Conclusions
• Infoscitex Background
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2
Project Team
• Infoscitex Corporation
– Principal Investigator: Andrew DeCarlo
Email: adecarlo@infoscitex.com / Phone: (781) 890-1338 x289
– Project Manager: Dr. Sherman Tyler
Email: styler@infoscitex.com / Phone: (781) 890-1338 x263
• Subcontractors
– Western Michigan University: Dr. Leszek Lilien
– Purdue University: Dr. Bharat Bhargava
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Agenda
• Project Team
• Technical Overview
• Task Summary and Discussion
• Future Work
• Conclusions
• Infoscitex Background
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4
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:
•
•
•
•
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Frequent link breakage
Inconsistent data rate
Incompatibility of resources
Temporary unavailability of needed resources and communication links
5
The Infoscitex Solution (Oppnets)
•
Opportunistic Resource Utilization
Networks (Oppnets) for UAV AdHoc 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
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– 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
6
The Oppnet Concept
Fighter
Satellites
X-47B UCAS
Seed Oppnet
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.
Radar
Processing
LCSs
Merchant
Ships
Target
Carrier
USVs
Underwater
Acoustic
Array
Oppnet links
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non-Oppnet UCAS links to Carrier
7
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
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Agenda
• Project Team
• Technical Overview
• Task Summary and Discussion
• Future Work
• Conclusions
• Infoscitex Background
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Phase I Milestone Schedule
Task
Milestone
Task 1
Task 2
Task 3
Task 4
Task O1
Milestone
Milestone
Milestone
Milestone
1
2
Base Mo.
3
4
5
6
Option Mo.
7 O1 O2 O3
Kickoff Meeting
Identify Key Use Cases
Develop Tactical Oppnet Capabilities
Test and Demonstrate Oppnets
Program Management and Reporting
Prepare for Phase II
Interim Status Reports
Final Review with Demo
Phase I Draft Final Report
Phase I Final Report
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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
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Oppnet Use Case Example
Fighter
Satellites
X-47B UCAS
Seed Oppnet
Radar
Processing
LCSs
Merchant
Ships
Target
Carrier
USVs
Underwater
Acoustic
Array
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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 fastmoving surface target
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Use Case Breakdown
18
UCAS
Seed Oppnet
17 43 45 51
46
47
48
50
19
20
44
Radar Plot Integrator
Helper 4
21 22 28
33 37 41
42
23
24
27
25
52
30
32
36b
38
40
F/A-18E Super Hornet,
Helper 7
49
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Radar Plot Analyzer
Helper 6
34
39
29
31 26
AEHF Satellites
Helper 5
36a
35
14
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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Task 2: Develop Tactical Oppnet Capabilities
• Lookup Subsequence
1) look up directory and identify reservist
helper
2) order to join
3) 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 job
6) helper sends result report
7) receive report from helper
OR:
receive and forward report
8) release helper (i.e., sends the release msg
to the helper).
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• Discovery Subsequence
0) failed look up for reservist helper
1) attempt discovery: scan & discovery
(are discovered non-reservists Oppnet-enabled or not?)
2) ask to join
3) agree to join or not; if agreed, joins and
is integrated into Oppnet
4) ask helper for (activity) report
OR:
ask for forwarding a task/message to
helper H and for H's (activity) report
5) helper does its job
6) helper sends result report
7) receive report from helper
OR:
receive and forward report
8) release helper
16
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
Seed Nodes
CC Nodes
Name
Functions
CTRL_start
Initiate Oppnet
CTRL_end
Terminate Oppnet
CTRL_cmd
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Send commend to seed
nodes
Name
Functions
SEED_scan
Scan communication spectrum to detect devices
that could become candidate helpers
SEED_discover
Discover candidate helpers with a specific
communication mechanism
SEED_listen
Receive and save messages in buffer
SEED_validate
Verify the received command
SEED_isMember
Checks if a device is already an Oppnet node
(Oppnet member)
SEED_evalAdmit
Evaluate a device and admit it into Oppnet if the
device meets criteria for admittance
SEED_sendTask
Send a task to other Oppnet device
SEED_delegateTask
Delegate a task that requires a permission from the
delegating entity
SEED_release
Release a helper when no longer needed
SEED_processMsg
Process a message from buffer
SEED_report
Report information to control center/coordinator
19
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 could
become candidate helpers (regular or lites)
HLPR_discover
Discover candidate helpers with a specified communication
mechanism
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.)
Name of the Primitive
Functions of the Primitive
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_assignStrongTa
sk
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
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Partial List of Helpers for Lightweight Nodes
Name of the Primitive
Functions of the Primitive
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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Task 2: Develop Tactical Oppnet Capabilities
• 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
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Task 3: Test and Demonstrate Oppnets
• Software Simulation
– Fine-tuning Oppnet implementation
– Providing information for customizing the
implementation per each use case
– Demonstrating feasibility
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Task 3: Test and Demonstrate Oppnets
Ac. Array
Ac. Array
UCAS
Speedboat
UCAS
F/A-18E
Speedboat
GOAL: Test the UCAS’ speed and accuracy in apprehending a fast-moving
speedboat without (left) and with (right) Oppnets helpers
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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/A18E 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.
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Simulation Random Variables
Random
Variable
Value Range
Statistical Value
Distribution
Description
DetectedSpeedbo
atPosition
xval: 0 AreaMaxX,
yval: 0 AreaMaxY
Uniform distribution
The position where an Acoustic Array detects the
speedboat is: (xval, yval).
FinalSpeedboatP
osition
xval : 0 AreaMaxX
Uniform distribution
The final speedboat position is: (xval, 100)
InitialFA18ExPosit
ion
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).
TimeToIntegrateF
A18EhelperByUc
as
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]
[2]
By simulation assumption, the yval of the point at which the F/A-18E enters AOR is 0.
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.
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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
Time till
Extended Oppnet
Detects Speedboat
successful speedboat
detection)
Average
Time
Standard
Deviation
Average
Time
Standard
Deviation
Average
Time
Standard
Deviation
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 Helper
vs. Delay in Speedboat Detection
Integration Delay
Integration Delay
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29
Varying Delay in Integrating Helper
vs. Success Ratios without and with Helper
Integration Delay
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Results: Varying Helper Density for
Speedboat Detection (Delays and Success Ratios)
Number of
Fighter
Helpers
Avg. for
Extended Oppnet
Success Ratio
Std Dev. for
Extended Oppnet
Success Ratio
Avg. 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
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Varying Helper Density vs. Success
Ratios without and with Helper (Delay Range 1)
Success ratio
70%
60%
50%
seed Oppnet success
ratio
40%
30%
Extended Oppnet success
ratio
20%
10%
Density of helpers
0%
1
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3
5
7
9
11
13
15
17
19
32
Varying Helper Density vs. Success
Ratios without and with Helper (Delay Ranges 2 and 5)
Success ratio
120%
100%
Seed Oppnet success
ratio
80%
60%
Extended Oppnet
success ratio
40%
20%
Density of helpers
0%
1
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3
5
7
9
11
13
15
17
19
33
Varying Helper Density vs. Helper Integration Delay and
Speedboat Detection Time (Delay Ranges 1 and 5)
Time
30.00
25.00
Average time till
Oppnet integrates
helper
Average time till
extended Oppnet
detects speedboat
(a)-(b)
20.00
15.00
10.00
5.00
Density of helpers
0.00
1
3
5
7
9
11
13
15
17
19
Time
12.00
10.00
Average time till
Oppnet integrates
helper
Average time till
extended Oppnet
detects speedboat
(a)-(b)
8.00
6.00
4.00
2.00
Density of helpers
0.00
1
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3
5
7
9
11
13
15
17
19
34
Single-Fighter Denial of Help
(Delays and Success Ratios)
Range for
Delay in
Integrating
Helper
Success Ratio
for
Seed Oppnet
Success Ratio for
Extended Oppnet
Success Ratio
for Extended
Oppnet – denial
of help with
probability 0.2
Success Ratio
for Extended
Oppnet –
denial of help
with probability
0.6
Success Ratio
for Extended
Oppnet – denial
of help with
probability 0.8
Range 1: [366]
27%
25%
20%
10%
5%
Range 2: [333]
27%
49%
39%
19%
9%
Range 3: [322]
27%
61%
51%
24%
11%
Range 4: [316]
27%
75%
60%
29%
13%
Range 5:
[3-13]
27%
85%
66%
32%
15%
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Single-Fighter Denial of Help
(Delays and Success Ratios)
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36
Multi-Fighter Denial of Help
(Delays and Success Ratios)
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
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
Range for
Delay in
Integrating
Helper
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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
37
Multi-Fighter Denial of Help (Delay Range 1)
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38
Multi-Fighter Denial of Help (Delay Range 2)
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Multi-Fighter Denial of Help (Delay Range 5)
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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-integrationdelay scenarios.
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Agenda
• Project Team
• Infoscitex Background
• Technical Overview
• Task Summary and Discussion
• Future Work
• Conclusions
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42
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.
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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
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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
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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
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46
Phase II Milestone Schedule
Milestone
Task 1
Task 2
Task 3
Task 4
Task 5
Task 6
Task 7
Task 8
Task 9
Milestone
Milestone
Milestone
Milestone
Task
Kickoff Meeting
User/Operation Requirements
Control/Seed OVM Primitives
Helper OVM Primitives
Lite OVM Primitives
Module Assembly and Debug
Simulation, Test, and Evaluation
Further RDT&E
Systems Integration
Project Management and Reporting
Interim Status Reports
Interim Review
Final Review/Demonstration
Phase II Final Report
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Month
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
47
Agenda
• Project Team
• Technical Overview
• Task Summary and Discussion
• Conclusions
• Infoscitex Background
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Agenda
• Project Team
• Technical Overview
• Task Summary and Discussion
• Future Work
• Conclusions
• Infoscitex Background
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49
Who We Are
• Engineering, Research and Development
– Develop advanced technologies
– Provide technical services
• Founded in 2000
• Small Business
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50
Corporate Vision
•
•
•
•
•
Customer Mission Focused
Preeminent Technology Development
Employee Excellence & Gratification
Community Involvement
Commitment to Longevity & Prosperity
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51
Corporate Timeline
IST acquired Systran
Federal
IST acquired
Foster-Miller’s
R&D Group
IST Energy
Corporation Spun
Out
IST Founded
1956
1977
Foster-Miller
Founded
2000
2005
2006 2008
Systran Federal
Founded
Infoscitex Corporation Ranked No.1 Fastest
Growing Private Company in New England 2008
3/23/2016
2009 Small Business of the Year by the Greater
Boston Chamber of Commerce
52
Performance
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53
Locations
Corporate Headquarters
Company Offices
External Facilities Agreements
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54
Capabilities
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
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
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•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
Intelligence Analysis
Intelligence Collection Management
Minimally Invasive Technologies
Modeling and Simulation Software
Development
Nanotechnology
Quality Assurance
R&D Information Technology Support
Robotics, Mechanisms, & Electromechanical Systems
Sensors & Data Acquisition
Signal Processing
Systems Protection
Tactical & Strategic Linguistics
Target Tracking
Thermal Management
User Interface Design
Weapon System Effectiveness
Wireless Communications
55
Laboratory Facilities
•
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
Engineering and Electronics:
•
Formal Outreach Relationships:
–
–
–
3/23/2016
Electronics Lab
Machine Shop
Mechanical Test Lab
Flight Simulation
Modeling & Simulation Suites
ATF Type 33 License - User of High
Explosives
Air Force Research Labs Human
Effectiveness Directorate
Naval Surface Warfare Center (China
Lake)
Colorado State University BSL3
Facilities (in progress)
56
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
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– 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
57
Academic Collaborators (primary)
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58
Example Product Success Story: SCRAMNet Technology
Sample Applications
•
•
•
•
•
•
•
•
•
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
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SCRAMNet-GT PCI
Productization Partner
59
Example Product Success Story: IPACK Technology
Sample Applications
•
•
•
•
•
•
•
•
•
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.
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IPACK/PCI Carrier
Productization Partners
60
Example Product Success Story: LinkXchange Technology
Sample Applications
•
•
•
•
•
•
•
•
•
•
Radar Test System, Aselsan A.S.
Reconfigurable Cockpit Simulator, Bell
757 Remote Control Landing System,
NASA- Langley
Cockpit Display System Lab, BoeingPhiladelphia
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.
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LX Switch Products
Productization Partner
61
Example Product Success Story: MBS Technology
Sample Applications
•
•
•
•
•
•
•
•
•
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.
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1553 BusXchange
Productization Partner
62
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
3/23/2016
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
3/23/2016
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
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65
Related Work
• Wireless Network Denial-ofService 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
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66