a distributed programming infrastructure for integrating smart sensors umakishore ramachandran,...
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A Distributed Programming Infrastructure for Integrating Smart SensorsUmakishore Ramachandran, Kenneth Mackenzie, Steve DeWeerth, Irfan Essa, Thad Starner
College of Computing, Georgia Institute of Technology
Problem: Octopus Apps!
SAMPLE APPLICATIONS• distributed collaboration• aware spaces• smart environments• monitoring, control• surveillance• emergency response
BASIC IDEA• emerging app class• tentacles: sensors, actuators• arms: data fusion, routing• head: cpu-intensive processing
FEATURES• distributed, pervasive infrastructure• of widely varying device capabilities• with control-loop flavor processing• on streams of varying bandwidths• requiring rapid response• at human perceptual speeds
Approach: Smart Plumbing
Stampede: SeamlessProgramming
Results:
Sensor Stack:
MediumAccessControl
DataServiceLayer
Data FusionLayer
Application
HelperServiceLayer
InfoExchange
Service
Hardware
Stack Diagram
MediumAccessError ControlRadio Control
RoutingFiltering
ScatterGather
In-networkFusion
Deployment, Monitoring
LocationService
Timing
InfoCollection
AccessControl,Attribute
Translation,Persistent database
Radio, Sensors, Memory, CPU
Functionality
GOALS• in-stack fusion• logical naming• application-awareness
OBSERVATIONS• concurrent apps• energy, net bandwidthconstraints
Media Broker:
DFuse:
TV Watcher:
• seamless programming• across diverse hardware• of compelling applications• reveals middleware requirements
REQUIREMENTS• support for physically distributed heterogeneous devices• easy access to compute-servers (clusters, grids)• diverse computation, communication and power capabilities• support for dynamic join/leave, registration, discovery• sophisticated stream management (fusion, type-baseddiscovery, publication, discovery, filter framework)
Broker federation
StampedeRegistry
audiovideo
Re-publish
transform
share
derive
• federated data distribution• publish/subscribe model• internal data broker threads• type-lattice based transcoding
stream registration andtransformation engine
“An Architecture for Event Web”Modahl, Bagrak, Wolenetz, Jain, RamachandranIEEE FTDCS ’04, Suzhou, China
“DFuse: A Framework for Distributed Data Fusion”Kumar, Wolenetz, Agarwalla, Shin, Hutto, Paul, RamachandranACM SenSys ’03, Los Angeles, California 2003
“Media Broker: An Architecture for Pervasive Computing”Modahl, Bagrak, Wolenetz, Hutto, RamachandranIEEE PerCom ’04, Orlando, Florida
http://www.cc.gatech.edu/~rama/ubiq-presence
Funded by NSF ITR/SY grant CCR-0121638
Event Web:
StreamServer
D-Stampede Cluster
Workstations
Media Capture Clients
Display Clients
www www www www www
Key
www
Generic Workstation
Video Capture System
Video Display Client
Web Results Clientwww
distributed media analysis and correlation
• architecture and application• automates stream capture, feature extraction, correlation• identifies most related streams
optimized fusion function placement in wired and wireless networks
Fusion Channel (a ‘Virtual Sensor’)
Producers
(sensors or other fusion channels)
Consumers
(actuators or other fusion channels)
. . . . . .
f()
DisplayFilter
Collage
Sources
S1
S2
S3
Task Graph
Testbed: IPAQ Farm
ROLE ASSIGNMENT• Naïve tree building• Optimization• Maintenance
Fusion Module
Placement Module
Resource Monitor,Routing Layer
Operating System
Hardware
Cost Function
(Minimize Transmission Cost)
0
10
20
30
40
50
60
70
80
90
100
Run Time(normalized)
Remaining Capacity(%)
Number of RoleTransfers (absolute)
MT2
MPV
MTP
simplified capture and rich access to structured media stores, organized around spatiotemporal events
Feature Extraction
i_conn
o_conn
domain
thread
Dynamic thread-channel graph
channel
BASIC IDEA• space-time memory• time-sequenced data streams•communication abstractions
• channels, queues, registers• distributed garbage collection• computation as thread-channel graph
1 2 3 54 6 7 8
KT=2
Garbage collection
ChangeDetection
Model 1Location
DigitizerVideoFrame
Histogram
MotionMask
TargetDetection
TargetDetection
HistogramModel
Model 2Location
Application: SmartKiosk People Tracker
Application
ResourceManagement
Sensor Access andManagement
Experiential EventWeb Browser
Feature Extraction and Event Generators
Query Server Event
BaseProgramming Abstractions
Media Streaming Engine
Sensor Network
Media Warehouse
Applications
Architecture
Domain Events(CS 6250 Lecture)
Elemental Events(Identity/Location)
Data Events(Face, Moving Lip Detectors)
Group MeetingTime: 10:00am-11:00am
Location: CCB201
Participants: Kishore Ramachandran, Ramesh Jain, Matthew Wolenetz