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Advanced media-oriented systems research: Ubiquitous capture, access, and interpretation. Faculty involved with RI-related projects

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advanced media oriented systems research ubiquitous capture access and interpretation
Advanced media-oriented systems research: Ubiquitous capture, access, and interpretation
  • Faculty involved with RI-related projects
    • Kishore Ramachandran, Mustaque Ahamad, Karsten Schwan, Richard Fujimoto, Ken Mackenzie, Sudha Yalamanchili, Irfan Essa, Jim Rehg, Gregory Abowd, Yannis Smaragdakis, Santosh Pande, Calton Pu, Ling Liu, ...
  • Federal funding
    • NSF RI, NSF ITR, DARPA, DOE
  • State funding
    • Yamacraw, GT Broadband Institute
  • Industry funding (equipment and personnel)
    • HP, Intel, Microsoft, IBM
mediabroker and dfuse powerful support for emerging applications

MediaBroker and DFuse:Powerful support for emerging applications

Kishore Ramachandran

Jim Rehg, Phil Hutto, Ken Mackenzie, Irfan Essa

College of Computing, Georgia Tech

Kath Knobe, Jamey Hicks, Raj Kumar

HP Labs

Students:

Rajnish Kumar, Matt Wolenetz, Ilya Bagrak, Martin Modahl, Bikash Agarwalla, Junsuk Shin, Arnab Paul, Sameer Adhikari, Nissim Harel, Hasnain Mandviwala, Yavor Angelov, David Hilley

slide4

Skiff

Skiff

  • Distributed system components
    • sensors, actuators, embedded processors, PDAs, laptops, clusters…

camera

camera

Sensor

Fusion

Sensors

Actuators

Unix / Linux / NT cluster

Data Aggregators

“OCTOPUS” DIAGRAM

head / arms / tentacles

killer app
Killer App?
  • Application context
    • distributed sensors with varying capabilities
    • control loop involving sensors, actuators
    • rapid response time at computational perception speeds
emerging applications
Emerging Applications
  • Distributed Collaboration
  • Emergency Response
    • Collaborative search and rescue
    • Evacuation management
  • “Aware” Environments

Key Requirements

  • Data distribution and Infrastructure adaptation
application characteristics
Application Characteristics
  • Physically distributed heterogeneous devices
  • Interfacing and integrating with the physical environment
  • Diverse stream types (low to high BW)
  • Diverse computation, communication and power capabilities (from embedded sensors to clusters)
  • Stream fusion/transformation, with loadable code
  • Resource scarcities
  • Dynamic join/leave of application components

Two projects:

  • MediaBroker
  • DFuse
abstractions abstractions

A Fusion Channel (a ‘Virtual Sensor’)

Inputs

(sensors or other fusion channels)

Consumers

(actuators or other fusion channels)

F()

. . .

. . .

Abstractions…abstractions
  • Goals:
    • Temporal guarantees, efficient streams, distributed synchronization, fusion support
    • Simplify application development
  • Key result: Fusion Channels
    • built on top of D-Stampede [ICDCS 2002]
the mediabroker
The MediaBroker
  • An architecture for Data Distribution
    • Type management, Scalability, Stream “sharing”
  • A sample scenario for distributed collaboration
    • Physically distributed participants in a classroom setting
    • Interaction via a variety of devices
    • System takes care of any needed data transformations and distribution

MediaBroker

Infrastructure

mediabroker infrastructure

audio

video

sonar

bio

fusion

derivation

sharing

registry

Re-publish

discovery

D-Stampede

virtual sensors

applications

MediaBroker Infrastructure( )
  • Fusion Channels and Virtual Sensors
  • Resource Discovery and Sharing
  • Sensor-Provided Attributes
  • Sensor-Derived Attributes
  • Scalability
  • Access Control and Resource Scheduling
  • Security
mediabroker and typed streams
MediaBroker and Typed Streams
  • Experimental “type-lattice” registry, discovery, and resolution structure

Fusion Channel

Performs text-to-speech

Fusion Channel

Outputs A/V as MPEG,

Incorporates

text as caption

Today’s

Lecture:

dfuse acm sensys 2003
DFuse [ACM SenSys 2003]
  • An architecture for Infrastructure Adaptation
  • A sample scenario for an aware environment
    • Field trip for a class!
    • Deployed power-constrained sensors
    • Dynamic wireless network consisting of the students’ PDAs
    • In-network stream filtering and aggregation
dfuse fundamentals

Sensors

Collage

Sink (Display)

Filter

Sample surveillance application task graph:

filter and collage are the fusion functions.

Cameras

  • Deployed on iPAQ farm!
  • Tested 4 cost-functions
DFuse Fundamentals
  • Fusion Module: Deploys task graph on sensor network
    • Comprehensive API for fusion and migration
    • Low-overhead
  • Placement Module: Employs a self-stabilizing algorithm to place fusion points in the network
    • Energy and application aware cost functions
    • Localized decisions