1. Stampede Overview Joint research between HP CRL and Georgia Tech (*) Kishore Ramachandran (*) Jim Rehg(*), Phil Hutto(*), Ken Mackenzie(*), Irfan Essa(*), Kath Knobe, Jamey Hicks Students (*) : Sameer Adhikari, Arnab Paul, Bikash Agarwalla, Matt Wolenetz, Nissim Harel, Hasnain Mandviwala, Yavor Angelov, Junsuk Shin, Rajnish Kumar, Ilya Bagrak, Martin Modahl, David Hilley

2. n Hardware Model l sensors, actuators, embedded processors, PDAs, laptops, clusters… “OCTOPUS” DIAGRAM head / arms / tentacles Skiff camera Data Aggregators Sensors Actuators Unix / Linux / NT cluster Channels / queues Sensor Fusion Distributed Ubiquitous Computing

3. Killer App? n Application context l distributed sensors with varying capabilities l control loop involving sensors, actuators l rapid response time at computational perception speeds

4. Application Scenarios n Mobile robots n Smart vehicles n Aware homes n Real-life emergencies l natural and man-made disaster response iearthquakes, twisters, fire, terrorist situations n Environmental monitoring l viruses, pollution, … l animals and birds in natural habitats n Augmented reality applications l training for hazardous situations l battlefield management n Interactive animation

5. Application Characteristics n Physically distributed heterogeneous devices n Distributed mobile sensing and actuation n Interfacing and integrating with the physical environment n Information acquisition, processing, synthesis, and correlation l streaming high BW data such as audio and video l low BW data such as from a haptic sensor l time-sequenced data n Dynamic computation continuum from low end device-level filtering to high end inference

6. Research Issues n Stream-oriented and time-sequenced data n Heterogeneity of Components n Resource management n High Availability n Clients leave and join arbitrarily n Security and Privacy

7. Stampede Project n Theme l seamless programming system spanning sensors and backend servers id-stampede: common programming paradigm across widely varying architectures [ICDCS 2002] isupports development of pervasive computing applications

8. Stampede computational model: a dynamic thread-channel graph thread Channel thread Channel i_conn o_conn many to many connections time sequenced data correlation of streams automatic GC put(ts, item) get(ts, item) consume(ts)

9. Experiences with Stampede n Color-based people tracker for SmartKiosk (Jim Rehg) Change Detection Model 1 Location Digitizer Video Frame Histogram Motion Mask Target Detection Target Detection Histogram Model Model 2 Location

10. Model 1 Model 2

11. Color-Based Tracking Example

12. n Video Textures (Irfan Essa) Generate an infinite video sequence from a finite set of video frames -embarrassingly parallel (comparison of images) -data distribution from source the main challenge -breaking image into strips to fit the computation in caches secondary challenge

13. Cluster skiff Stampede client (C) Stampede Application (C) skiff Stampede client (C) STM.... n Multipoint video/audio capture

14. Multipoint Video Demo

15. Ongoing Work n Media broker architecture l resource naming and discovery l data fusion (fusion channels) l asynchronous notification n Aspect-oriented programming support l STAGES language and compiler n Dynamic multi-cluster implementation n D-Stampede Web Service l.NET implementation n Models for reasoning about failures n Security and privacy issues