April 20, 2011 CSS Faculty Research Talk 1

Sensor Grid IntegrationAn Agent-Based Workbench for On-the-Fly Sensor-Data Analysis

Munehiro Fukuda, Ph.D.Distributed Systems Laboratory, Computing & Software Systems University of Washington Bothell

April 20, 2011 CSS Faculty Research Talk 2

Parallel Computing Opportunities Computation-intensive opportunities using live data

1. Agriculture - frost protectionQ: How would you predict air temperature at each point of orchards for the

next 4, 8, and 12 hours, based on past/current temperature data?A: Air temperature interpolation and prediction

2. Content-based video/image analysisQ: How would you instantly find harvestable trees and empty parking

spots, based on remote video/image data? A: Parallel color-based video/image-content analysis

3. Driving-time estimationQ: How would you accurately estimate your driving time when caught in a

traffic jam?A: Real-time traffic simulation

April 20, 2011 CSS Faculty Research Talk 3

Hurdles to Parallelization From user perspective:

Yet far reaches of computing power1. Grid/Cloud yet incapable of facilitating tailor-made parallel

computation for individual-provided real-time data2. Manual set-up between computation (multi-core PCs) and

peripheral I/Os (sensors and cells) at an individual level

From developer perspective: Strong allergy to parallel computing

1. Steep learning curve of the concept and usage regarding multi-processes and threads

2. Difficulty in enforcing proper synchronization

April 20, 2011 CSS Faculty Research Talk 4

3. Elastic Data Channels

An Architecture for On-the-Fly Sensor-Data Analysis

water level sensors

temperature sensors

Cluster A

Cluster B

Storage X Y Z

User 1

User 2

wave model

heat model

1. Field-based resource allocation

2. Multi-Agent Spatial Simulation

April 20, 2011 CSS Faculty Research Talk 5

Research Contribution Field-based process dispatch and migration

Bottom-up self-organizing resource advertisement Top-down self-adapting resource acquirement Nomadic-process tracking and broken-link recovery

Multi-process, multithreaded library for multi-agent spatial simulation No awareness of processes, threads, and race conditions Dynamic load balancing Dynamic computation scale-up and down

Delivery of sensor-data and computation outputs Stream-oriented file-based delivery to nomadic computation Graphical outputs to mobile users

April 20, 2011 CSS Faculty Research Talk 6

Field-Based Process Dispatch & Migration Self-Adapting and Self-Organizing Approaches

UW BothellInternet

Applications

Bottom-up self-organizing computation-resource potential field

Top-down self-adapting resource allocation

Shizuoka University

April 20, 2011 CSS Faculty Research Talk 7network segment B network segment Cnetwork segment A

computingnode

tcplink/udp_relaybricks

tcplink/udp_relaynode

tcplink/udp_relaynode

vlink

vlink

vlink

potential fieldagent

migrationsentinelagent

rank 0 2 31

Layer 0: Hardware

Layer 1: TCP-link-assisted UDP-broadcast space

Layer 2: UWAgentsdaemondaemon

Layer 3: Computing-Resource Potential Field

Layer 4: Sentinel Agents

Layer 5: Applications (MPI, MASS)

AgentTeamwork-Lite

April 20, 2011 CSS Faculty Research Talk 8

MASS: Library for Multi-Agent Spatial SimulationExecution Model

April 20, 2011 CSS Faculty Research Talk 9

1. import MASS.*; // Library for Multi-Agent Spatial Data Analysis and Simulation 2. import java.util.Vector; // for Vector 3. 4. public class TemperatureAnalysis { 5. public static void main( String[] args ) { 6. // validate the arguments 7. int size = Integer.parseInt( args[0] ); 8. int nFlows = Integer.parseInt( args[1] ); 9. int maxTime = Integer.parseInt( args[2] ); 10. 11. MASS.init( args ); // start MASS 12. 13. // create a virtual orchard in a distributed array. 14. Places orchard = new Places( 1, ”Orchard", null, size, size ); 15. // populate agents that behave as airflows over the orchard. 16. Agents airflows = new Agents( 2, ”AirFlow", null, orchard, nFlows); 17. 18. // define the four neighbors of each cell 19. Vector<int> neighbors = new Vector<int>( ); 20. int[] north = { 0, -1 }; neighbors.add( north ); 21. int[] east = { 1, 0 }; neighbors.add( east ); 22. int[] south = { 0, 1 }; neighbors.add( south ); 23. int[] west = { -1, 0 }; neighbors.add( west ); 24. 25. // now go into a cyclic analysis 26. for ( int time = 0; time < maxTime; time++ ) { 27. // exchange temperature and estimate new temperature 28. orchard.exchangeAll( 1, Orchard.exchange, neighbors ); 29. orchard.callAll( Orchard.update ); 30. 31. // move airflows to a neighbor if necessary 32. airflows.callAll( Airflow.decideNewPosition ); 33. airflows.manageAll( ); 34. } 35. MASS.finish( ); // finish MASS 36. } 37. }

MASS Programming Style

April 20, 2011 CSS Faculty Research Talk 10

rank 0 2 31

Major underlying network protocols converted to FileInput/OutputStream System.in/out/err and Graphics forwarded to a remote user Connection resumed upon a job migration

WiFi sensor network

418/900MHz sensor network

Access point

Sink point

cluster

SNMT/UDP BroadcastFileInput/OutpuStream

SSH Tunnel

SSH Tunnel

FileInput/OutputStream

Storage

User

HTTP client

HTTP server

Sensor server (FTP)

SFTP ServerSFTP Client

SSH Tunnel stdin, stdout, and X

SSH Tunnel

Connectors: Flexible Data Cannel

FileInput/OutpuStream

System.in/out/errGraphics

FTP Client

rank 0 2 31Job migration

April 20, 2011 CSS Faculty Research Talk 11

Programming with Connectors

FTP Server

sensor1

Access point

SNMT/UDP Broadcast

Sensor Server

sensor2

April 20, 2011 CSS Faculty Research Talk 12

Application 1Temperature Analysis

temperature sensor network

Sink point

Step 1 (estimating current spatial state):Interpolating temperatures with

- inverse distance weighting (http://en.wikipedia.org/wiki/Inverse_distance_weighting)

- polynomial regression (http://www.kdkeys.net/)

Step 2 (predicting future spatial state):Simulating heat propagation with heat equation (Dr. Terrel: http://www.math.cornell.edu/~bterrell/free.html)

orPredicting temperature with artificial neural network (Smith, McClendon, Hoogenboom: Improving Air Temperature Prediction with Artificial Neural Networks, Int’l Journal of Computational Intelligence Vol3 No3)

April 20, 2011 CSS Faculty Research Talk 13

Application 2Traffic Simulation

Webcam netwrok

HTTP server

Step 1 (retrieving current traffic state):Initializing a traffic simulation with web-cam/real-time GIS dataas well as receiving a travel itinerary

Step 2 (estimating travel time):Calculating accurate travel time from traffic simulation(Multi-Agent Transport Simulation Toolkit, http://matsim.org/)

DB servers

Userstart

goal

start

goal

45 minutes

Note: these two graphics are snapshots of MATSIM execution.

April 20, 2011 CSS Faculty Research Talk 14

Application 3Color-Based Video/Image Analysis

Web cam network

Sink point

Step 1 (analyzing photos in color):Analyzing each photo with Mansell color system(Dr. Kiyoki and Dr. Kurabayashi: http://www.mdbl.sfc.keio.ac.jp)

Step 2 (sorting them):Based on matching counts of Mansell color patterns

…

1 2 3 …

April 20, 2011 CSS Faculty Research Talk 15

Group Members

Tim Clapp CSS497: Porting Java Grande Benchmark to MASS and measuring the MASS performance

April 20, 2011 CSS Faculty Research Talk 16

Research Items to Be Done1. AgentTeamwork-Lite

Job deployment/migration policies: rate-based, history-based, job-duration-based, etc. Job check-pointing: user-initiated, system-initiated, check-pointing frequency, etc.

2. MASS Library Different platforms: multi-process, multithreaded, and Java/C++-based versions Dynamic load balancing: changing node boundary and #nodes engaged in computation Performance evaluation: using benchmark programs and applications

3. Connector Stream-based channels from sensors through cloud jobs to a user laptop. Channel hand off to nomadic jobs and mobile users Sensor data presentation (from raw text data to XMLs)

4. Applications Temperature prediction: artificial neural network, empirically-obtained formulae, and

graphical outputs Traffic simulation: MatSim (partially working) Video/image analysis: color-based content analysis, etc. Programmability and performance evaluation

April 20, 2011 CSS Faculty Research Talk 17

Correlation between Coursework and Research

Research projects CSS434 CSS534

AgentTeamwork-Lite HW3: RMI v.s. mobile agentsWeek 10: Grid Computing

Week 7: Job ManagementWeek 8: File ManagementWeek 9: Fault Tolerance

MASS Library Week 5: OS Supports Week 1-2: OpenMPWeek 3-4: MPIWeek 4-5: Paradigm Oriented Programming

Connector HW1: Chat server Week 9: Fault Tolerance(CSS543: Sockets)

Applications HW2: MPI Java programs HW1: TSPHW2: Molecular DynamicsHW3: Inverted IndexesWeek 6: Parallel Programming Patterns

April 20, 2011 CSS Faculty Research Talk 18

Keys to Success Targeting sensor networks in practical use

Collaboration with Mr. Eliot, Valhalla-Wireless and Mr. Doornink, WA State Tree Fruit Research Commission

Receiving proper and timely inputs from sensor-network specialists

Collaboration with at other universities: Shizuoka and Keio

Getting funded NSF CSR core program

UW Business Plan Competition with AgComm and UWS MBAAdvanced to Investment Round on April 27 at the Bell Harbor International Conference Center in downtown Seattle.

Waiting for hopefully good news