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TeamTrak: A Test Bed for Mobile Ad-Hoc Networks

TeamTrak: A Test Bed for Mobile Ad-Hoc Networks. Hardware/software test bed to enable a variety of projects in wireless, mobile, social, and geo- computing. Hardware: 32 tablet PCs plus with sensor helmet (GPS + compass + camera) and accelerometer on the foot.

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TeamTrak: A Test Bed for Mobile Ad-Hoc Networks

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  1. TeamTrak: A Test Bed forMobile Ad-Hoc Networks • Hardware/software test bed to enable a variety of projects in wireless, mobile, social, and geo- computing. • Hardware: 32 tablet PCs plus with sensor helmet (GPS + compass + camera) and accelerometer on the foot. • Software: Collects sensor data, shares data with neighbors via multi-hop ad-hoc network over WiFi. • TeamTrak allows us to explore concepts relevant to current and proposed mobile computing systems: • Cellular phones reporting sensor data. • Mobile cartography data collection units. • US Army Future Force Warrior. • Our focus is on the algorithms, systems, and software, using simple commodity hardware.

  2. USB Hub Tablet PC Garmin GPS-18 PNI V2Xe Compass Watchport USB Camera Pedometer (3-axis accel) TeamTrak uses cheap commodity equipment and software, so it is easy to swap in a higher quality camera, newer PC, etc.

  3. Research Challenges in TeamTrak • Robust Navigation: • Problem: GPS works fine on the open road, but is very inaccurate when obstructed by trees and buildings. • Solution: Share multiple sources of location data over the network to improve location quality: e.g. peer GPS, pedometer, compass, fixed bases, (road signs?) • Mining Mobile Social Networks: • Problem: How do humans self-organize, share information? How do emergencies influence human behavior? What patterns can be inferred for an autonomic, dynamic, and reactive system? • Solution: Design efficient learning and predictive algorithms to discover community structures and anomalous. Integrate data collection, analysis and discovery into an action-oriented predictive framework. • Managing Large-Scale Image Sets: • Problem: It is very easy to acquire TB of image data, but it is much harder to store, manage, and explore it. Bottleneck is I/O bandwidth. • Solution: Employ massively parallel active storage clusters to archive, index, and search large datasets. Move small code to large data, instead of vice versa. Provide new languages for manipulation

  4. People Involved in TeamTrak • Prof. Douglas Thain • Faculty in distributed systems and storage systems. • Prof. Christian Poellabauer • Faculty in mobile and real time systems. • Prof. Nitesh Chawla • Faculty in machine learning and data mining. • Maj. Jeffrey Hemmes, USAF • Ph.D student studying robust navigation. • Rory Carmichael • B.S. student working on testing and image acquisition. http://www.nd.edu/~teamtrak

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