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

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 CSS Faculty Research Talk

2. Parallel Computing Opportunities • Computation-intensive opportunities using live data • Agriculture - frost protection Q: 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 • Content-based video/image analysis Q: 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 • Driving-time estimation Q: How would you accurately estimate your driving time when caught in a traffic jam? A: Real-time traffic simulation CSS Faculty Research Talk

3. Hurdles to Parallelization • From user perspective: • Yet far reaches of computing power • Grid/Cloud yet incapable of facilitating tailor-made parallel computation for individual-provided real-time data • 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 • Steep learning curve of the concept and usage regarding multi-processes and threads • Difficulty in enforcing proper synchronization CSS Faculty Research Talk

4. An Architecture for On-the-Fly Sensor-Data Analysis 2. Multi-Agent Spatial Simulation Storage X Y Z wave model 3. Elastic Data Channels water level sensors User 1 Cluster A heat model User 2 Cluster B temperature sensors 1. Field-based resource allocation 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 CSS Faculty Research Talk

6. Field-Based Process Dispatch & MigrationSelf-Adapting and Self-Organizing Approaches Applications Top-down self-adapting resource allocation Bottom-up self-organizing computation-resource potential field Shizuoka University UW Bothell Internet CSS Faculty Research Talk

7. AgentTeamwork-Lite rank 0 1 2 3 Layer 5: Applications (MPI, MASS) Layer 4: Sentinel Agents sentinel agent migration Layer 3: Computing-Resource Potential Field potential field agent Layer 2: UWAgents daemon daemon Layer 1: TCP-link-assisted UDP-broadcast space vlink vlink tcplink/udp_relay node tcplink/udp_relay bricks tcplink/udp_relay node vlink Layer 0: Hardware computing node network segment A network segment B network segment C CSS Faculty Research Talk

8. MASS: Library for Multi-Agent Spatial SimulationExecution Model CSS Faculty Research Talk

9. MASS Programming Style 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. } CSS Faculty Research Talk

10. 1 1 2 2 3 3 rank 0 rank 0 Connectors: Flexible Data Cannel • 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 Sensor server (FTP) SSH Tunnel SFTP Server FTP Client SFTP Client Storage SNMT/UDP Broadcast SSH Tunnel WiFi sensor network FileInput/OutpuStream FileInput/OutputStream Access point Job migration FileInput/OutpuStream cluster SSH Tunnel stdin, stdout, and X HTTP client System.in/out/err Graphics 418/900MHz sensor network SSH Tunnel User Sink point HTTP server CSS Faculty Research Talk

11. Programming with Connectors Sensor Server sensor1 sensor2 FTP Server Access point SNMT/UDP Broadcast CSS Faculty Research Talk

12. Application 1Temperature Analysis 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/) temperature sensor network Sink point Step 2 (predicting future spatial state): Simulating heat propagation with heat equation (Dr. Terrel: http://www.math.cornell.edu/~bterrell/free.html) or Predicting temperature with artificial neural network (Smith, McClendon, Hoogenboom: Improving Air Temperature Prediction with Artificial Neural Networks, Int’l Journal of Computational Intelligence Vol3 No3) CSS Faculty Research Talk

13. goal start goal 45 minutes start Application 2Traffic Simulation Step 1 (retrieving current traffic state): Initializing a traffic simulation with web-cam/real-time GIS data as well as receiving a travel itinerary DB servers Webcam netwrok HTTP server User Step 2 (estimating travel time): Calculating accurate travel time from traffic simulation (Multi-Agent Transport Simulation Toolkit, http://matsim.org/) Note: these two graphics are snapshots of MATSIM execution. CSS Faculty Research Talk

14. Application 3Color-Based Video/Image Analysis 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) Web cam network Sink point … Step 2 (sorting them): Based on matching counts of Mansell color patterns 1 2 3 … CSS Faculty Research Talk

15. Group Members Tim Clapp CSS497: Porting Java Grande Benchmark to MASS and measuring the MASS performance CSS Faculty Research Talk

16. Research Items to Be Done • 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. • 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 • 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) • 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 CSS Faculty Research Talk

17. Correlation between Coursework and Research 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 MBA Waiting for hopefully good news Advanced to Investment Round on April 27 at the Bell Harbor International Conference Center in downtown Seattle. CSS Faculty Research Talk