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ONR Meeting Aug 4, 2005

ONR Meeting Aug 4, 2005. 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

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ONR Meeting Aug 4, 2005

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  1. ONR Meeting Aug 4, 2005 • 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 • 9:55 - 10:15 Break • 10:15 – 10: 40 Models/Simul Rajive • 10:40 – 11:05 Mobile BBN, QoS MAC Izhak • 11: 05 – 11: 30 Video Encoding John Villa • 11:30 - 11: 55 Stress testing; GUR Len • 11:55 – 12:40 Lunch in 4760

  2. Agenda (cont) • Afternoon Lab visits/demos in individual PI labs • 12:40 –1:20 Simulation Lab Rajive • 1:20 – 2:00 Sensor Lab Mani & John Villa • 2:00 – 2:40 Networking Lab Mario • 2:40– 3:20 Mobility/Net mngt Lab Izhak • 3:20 – 3: 50 Break (in Rice Room 6764) • 3: 50 – 4:30 Radio Lab Babak • 4:30 – 5:30 Debriefing Rice Room 6764 • 6:30 - 8:00 Dinner in Westwood

  3. The AINS Program at UCLAAINS 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

  4. SURVEILLANCE MISSION AIR-TO-AIR MISSION STRIKE MISSION RESUPPLY MISSION FRIENDLY GROUND CONTROL (MOBILE) SATELLITE COMMS SURVEILLANCE MISSION UAV-UAV NETWORK COMM/TASKING COMM/TASKING Unmanned UAV-UGV NETWORK Control Platform COMM/TASKING Manned Control Platform Algorithms and Protocols for a Network of Autonomous Agents

  5. Multilayer Architecture FLIR

  6. 20 Mbps 10 Mbps 300 kbps 2 Mbps 128 kbps 128 kbps 20 kbps 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

  7. Mobile Sensors Provide Situational-Awareness

  8. Hierarchical Configuration of UV-aided Mobile Backbone Network (UV-MBN) ANet 3 ASPN 1 ASPN 2 ANet 1 Backbone Node Gateway ANet 2

  9. Swarm Multicast Swarm Leader swarm Command post

  10. Video from sensors to Commander FLIR

  11. Unique Image Processing/Analysis Capabilities • “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

  12. Hybrid Simulation and evaluation component Simulated large-scale network Access Nodes & Hybrid Simulation Server Cluster Small-scale Real Testbed • Internet

  13. 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

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