ONR Meeting Aug 4, 2005
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All meetings in RICE Room 6764 BH, or in PIs Labs (afternoon demos)
8:00 – 8:30 Breakfast
8:30 – 8:40 Welcome, Intro
Mario
8:40 – 9:05 Radios
Babak
9:05 – 9:30 Sensor nets
Mani
9:30 – 9:55 Scalable Nets
Mario
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9:55 - 10:15
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10:15 – 10: 40 Models/Simul
10:40 – 11:05 Mobile BBN, QoS MAC
11: 05 – 11: 30 Video Encoding
11:30 - 11: 55 Stress testing; GUR
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11:55 – 12:40
Break
Lunch in 4760
Rajive
Izhak
John Villa
Len
Agenda (cont)
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Afternoon Lab visits/demos in individual PI labs
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12:40 –1:20
1:20 – 2:00
2:00 – 2:40
2:40– 3:20
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3:20 – 3: 50 Break (in Rice Room 6764)
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3: 50 – 4:30
4:30 – 5:30
Radio Lab
Debriefing
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6:30 - 8:00
Dinner in Westwood
Simulation Lab
Sensor Lab
Networking Lab
Mobility/Net mngt Lab
Rajive
Mani & John Villa
Mario
Izhak
Babak
Rice Room 6764
The AINS Program at UCLA
AINS review, Aug 4, 2004
• 5 year research program (Dec 2000 – Dec 2005)
• 7 Faculty Participants: 3 in CS Dept, 4 in EE Dept
• Goal: design a robust, self-configurable, scalable
network architecture for intelligent, autonomous
mobile agents
SATELLITE
COMMS
SURVEILLANCE
MISSION
SURVEILLANCE
MISSION
UAV-UAV NETWORK
AIR-TO-AIR
MISSION
STRIKE
MISSION
COMM/TASKING
Unmanned
Control Platform
COMM/TASKING
COMM/TASKING
RESUPPLY
MISSION
UAV-UGV NETWORK
FRIENDLY
GROUND CONTROL
(MOBILE)
Manned
Control Platform
Algorithms and Protocols for a Network of
Autonomous Agents
Multilayer Architecture
FLIR
The Radio Component
The need for an innovative radio solution
• Wide diversity of radio requirements across network –
(bandwidths, ranges, channels)
• Enemy Jamming, LPI, LPD
• Adaptive to fast-fading channel impairments
• Technology: OFDM MIMO based systolic radios
20 Mbps
10 Mbps
300 kbps
20 kbps
2 Mbps
128 kbps
128 kbps
Mobile Sensors Provide SituationalAwareness
Hierarchical Configuration of UV-aided
Mobile Backbone Network (UV-MBN)
ANet 1
Backbone Node
Gateway
ANet 2
ANet 3
ASPN 1
ASPN 2
Swarm Multicast
Swarm
Leader
swarm
Command post
Video from sensors to
Commander
FLIR
Unique Image Processing/Analysis
Capabilities
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“Region of Interest” (ROI)
capabilities enabling much higher
resolution in one or more areas of
the image – enables targets to be
readily tracked and identified
• “Mosaicking of images in
error-prone transmission
environments
Hybrid Simulation and
evaluation component
Simulated large-scale network
Access Nodes & Hybrid Simulation Server Cluster
Small-scale Real Testbed
Internet
Stress Testing
• Find natural limits:
• of the single components
• of the integrated network
• understand how an attacker can (even
partially) destroy the network
• make the networks more resilient to
attacks