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SunHee Yoon and Cyrus Shahabi University of Southern California

The C lustered AG gregation Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks. SunHee Yoon and Cyrus Shahabi University of Southern California ACM Transactions on Sensor Networks 2007. Overview.

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SunHee Yoon and Cyrus Shahabi University of Southern California

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  1. The Clustered AGgregation Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks SunHee Yoon and Cyrus Shahabi University of Southern California ACM Transactions on Sensor Networks 2007

  2. Overview Exploiting Spatial Correlation Towards an Energy Efficient Clustered AGgregation Technique (CAG) IEEE International Conference on Communications 2005 The Clustered AGgregation (CAG) Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks ACM Transactions on Sensor Networks 2007 Addition: Contour Maps: Monitoring and Diagnosis in Sensor Networks The International Journal of Computer and Telecommunications Networking, 2006

  3. Outline • Introduction • The CAG algorithm • Measurement and Correlation Model • Experimental Study • Conclusion and Future Work • additional • Contour Maps vs. CAG

  4. Introduction (1/3) • Efficient in-network aggregation • In-network query processing and data aggregation • save energy, and reduce computation • monitoring applications of WSNs • Tobler’s first law of geography • Everything is related to everything else, but near things are more related than distant things. • 每件事物彼此之間都會有相關，但是距離近的事物會比遠的事物相關性更高。

  5. Introduction (2/3) • CAG • Clustered AGgregation algorithm • leveraging spatial and temporal correlation • forms clusters of sensor nodes • transmits a single value per cluster • similar to Tiny AGgregation (TAG) • CAG can achieve energy-savings. • reduce the number of transmissions • incur a small error in the query result TAG: Tiny AGgregation service for ad-hoc sensor networks, OSDI, 2002

  6. Introduction (3/3) • User-provided error threshold: • accuracy requirement parameter • bound the difference among the sensor readings in a cluster • approximate answer always stays within the error threshold of the correct answer • Two phases • Query and Response • Two modes • Interactive Mode, Streaming Mode

  7. The CAG algorithmTwo Modes of CAG Operation Interactive mode Streaming mode responding with a single value! periodic response messages Exploits only the spatial correlation.

  8. The CAG algorithmInteractive Mode Base Station User Query Query Packet Query Packet Query Packet MR MR : user-provided error threshold : My local sensor Reading MR : Cluster-head sensor Reading

  9. The CAG algorithmInteractive Mode ? ? Only cluster-head transmits its sensing value!

  10. The CAG algorithmInteractive Mode 51 47 Example: 49.5 AVG (43, 59) = ? AVG (51, 47, 50) = ? AVG (30, 64) = ?

  11. The CAG algorithmInteractive Mode • Problems • Cannot provide the results with the bounded error. • duplicate sensitive vs. duplicate insensitive • Does not consider the size of cluster. • Cannot take advantage of the temporal correlation. • Properties of Aggregate Operators

  12. The CAG algorithmStreaming Mode • A single query generates periodic responses from the network. • spatial correlation and temporal correlation • epoch duration: i • To generate a query reply every i seconds. • Compare to the Interactive Mode • periodic response vs. one-shot response • The clusters need to be updated and repaired. • allow cluster size estimation

  13. The CAG algorithmStreaming Mode: Cluster Adjustment Cluster Adjustment Interval…

  14. The CAG algorithmStreaming Mode: Cluster Adjustment • Adjustment cost can be controlled. • The cluster adjustment messages only propagate to the nodes within the same cluster. • The parent node always performs cluster adjustment before its children. • Cluster Adjustment Interval • maximum amount of time that data can be out-of-range • A smaller interval makes system more responsive to the data dynamics.

  15. The CAG algorithmStreaming Mode: Cluster Size Estimation • Errors in the result obtained from the interactive mode can be large. • Equal weights are assigned to clusters of different size. • Estimate the size of the cluster. • Too costly in the interactive mode, but practical in the streaming mode. • With high temporal and spatial correlations… • Cluster adjustment is infrequent. • Cluster size estimation overhead is small.

  16. The CAG algorithmStreaming Mode: Cluster Size Estimation Example: Count the number of count increment messages… count increment message count decrement message cluster adjustment!

  17. Measurement and Correlation ModelVariogram Models • The variogram is defined as follows: • h: distance • X(p) and X(p+h): values of each pair of points at distance h • Three common variogram models • Spherical Model • Linear Model • Fractal Model

  18. Measurement and Correlation ModelVariogram Models • Spherical Model • increases linearly in the beginning, then becomes a sill • Data is correlated over a shorter distance than others. • Linear Model • Data becomes less correlated as distance increases. • Fractal Model • ubiquitous in nature • should be linear in a graph of against

  19. Measurement and Correlation ModelData Sets • Sensor Data Measurement in a Regular Grid • two different environments • light and temperature • mica2 motes and MTS 300 sensor boards Outdoor: Exposition Park in L.A. Indoor: 4th floor of Tutor Hall at USC

  20. Measurement and Correlation ModelData Sets • Data with Irregular Mote Placement • Great Duck Island • humidity, temperature, light, and pressure • Irregular inter-node distance are subdivided into a number of intervals called logs. Sensor Deployment Map of Great Duck Island

  21. Measurement and Correlation ModelData Sets • Synthetic Data from the Statistical Model • Reference: Modeling Spatially-correlated Sensor Network Data, SECON, 2004 • 250m x 250m grid • Parameters: • larger h results in higher spatial correlation 7h data 9h data

  22. Measurement and Correlation ModelData Sets • Synthetic Data from the Ecological Model • 250m x 250m grid • Similar to the fractal pattern found in the environment. • Fractal Dimension = 2 • Spatial Pattern Data High correlation level: between 7h and 9h

  23. Measurement and Correlation ModelThe Spatial Data Model • Apply temperature data to the correlation model. • Linear model: • Spherical model: • Quasi-spherical model:

  24. Measurement and Correlation ModelThe Spatial Data Model Variogram Temperature data from Exposition Park Light from Exposition Park Temperature from Exposition Park spherical characteristic spherical characteristic linear functionality linear functionality linear functionality

  25. Experimental StudyEvaluation Metrics and Experimental Setup • Reduced number of transmissions • interactive mode: • in the streaming mode • Accuracy of result

  26. Experimental StudyEvaluation Metrics and Experimental Setup • TOSSIM simulator of TinyOS 1.1.8 • Temperature Data • 1, 4, 6, and 7 PM from Exposition Park • Temperature readings from Great Duck Island • Three different deployment densities. • 250m x 250m grid • dense: 550 nodes (26 neighbors per node) • moderate: 375 nodes (17 neighbors per node) • sparse: 200 nodes (9 neighbors per node) • Two types of topology. • lossless and empirical loss rates • = 0, 2, 4, 10, and 20%

  27. Experimental StudyExperimental Results: Interactive Mode • 375 nodes • 250m x 250m • = 20% • 9h synthetic data Most new clusters are built along the diagonal band! Root Node

  28. Experimental StudyExperimental Results: Interactive Mode Improved performance of CAG compared to TAG Test Data: Temperature from Exposition Park 51.25% 37.5%

  29. Experimental StudyExperimental Results: Interactive Mode Precision with empirical radio profile Test Data: Temperature from Exposition Park 9.375% Error out-of-bound

  30. Experimental StudyExperimental Results: Interactive Mode Precision with perfect link reliability Test Data: Temperature from Exposition Park The temperature data in the physical world follows the normal distribution.

  31. Experimental StudyExperimental Results: Streaming Mode • Three different data sets for measurement study. • Great Duck Island Dataset • 35 nodes • temperature data (recorded once per hour) • four consecutive days • Stair-wise Dataset • from Exposition Park • temperature readings between 4 PM and 6 PM • Linear Dataset • from Exposition Park • temperature snapshots at 4 PM and 6 PM • Generate a linear dataset by linearly interpolated.

  32. Experimental StudyExperimental Results: Streaming Mode Total transmissions overhead between TAG and CAG Test Data: Temperature snapshot from Exposition Park (accumulated) 63.07% reduction 70.24% reduction

  33. Experimental StudyExperimental Results: Streaming Mode Total transmissions overhead between TAG and CAG Test Data: Temperature from Great Duck Island 19.0% reduction (accumulated) Less nodes send their responses to the root!

  34. Experimental StudyExperimental Results: Streaming Mode Cluster adjustment overhead: Query Flooding and CAG Test Data: Temperature from Exposition Park (Linear Data Set) 20000 (accumulated) 52 Both algorithms are reclustering at the same frequency!

  35. Experimental StudyExperimental Results: Streaming Mode Cluster adjustment overhead: Query Flooding and CAG Test Data: Temperature from Great Duck Island unacceptable overhead… 6650 (accumulated) local repair vs. global adjustment 249

  36. Experimental StudyExperimental Results: Streaming Mode Cluster adjustment overhead: Linear and Stair-wise Test Data: Temperature snapshot from Exposition Park (accumulated) cluster adjustment Cluster adjustment is continuous!

  37. Experimental StudyExperimental Results: Streaming Mode Breakdown of transmission overhead Test Data: Temperature from Exposition Park (Linear Data Set) 987 76

  38. Experimental StudyExperimental Results: Streaming Mode Breakdown of transmission overhead Test Data: Temperature from Great Duck Island flat decrease vs. gradual decrease 1531 229

  39. Experimental StudyExperimental Results: Streaming Mode The accuracy of result achieved Test Data: Temperature from Exposition Park (Linear Data Set) downward trend! 3.09%

  40. Experimental StudyExperimental Results: Streaming Mode The accuracy of result achieved Test Data: Temperature from Great Duck Island Error out-of-bound! 6.26%

  41. Conclusion and Future Work • Clustered AGgregation Technique • energy-efficient in-network aggregation • leveraging both spatial and temporal correlations • resilient to the packet loss • ensure bounded approximation • We would like to extend this work. • hybrid clustering protocol • Provide proactive and reactive data acquisition.

  42. additional. Contour Maps vs. CAG Sensor nodes that actually sent out reports. not sufficiently sampled! more evenly sampled! Contour Maps CAG Contour Maps: Monitoring and Diagnosis in Sensor Networks

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