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Shape Segmentation and Applications in Sensor Networks

Shape Segmentation and Applications in Sensor Networks. Xianjin Zhu Rik Sarkar Jie Gao. INFOCOM 2007. Lee, sunkyung. Contents. Introduction Background Algorithm Outline Segmentation algorithm Application Conclusion. Introduction(1/6). irregular sensor field !.

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Shape Segmentation and Applications in Sensor Networks

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  1. Shape Segmentation and Applications in Sensor Networks Xianjin Zhu RikSarkarJieGao INFOCOM 2007 Lee, sunkyung

  2. Contents Introduction Background Algorithm Outline Segmentation algorithm Application Conclusion ⓒ KAIST SE LAB 2008

  3. Introduction(1/6) irregular sensor field ! • Common assumption • Sensor nodes are deployed uniformly inside a simple geometric region • e.g) square • In practice, can be complex • Obstacles (lakes, buildings) • Terrain variation ⓒ KAIST SE LAB 2008

  4. Introduction(2/6) • Some protocols may fail • Greedy forwarding : packets are greedily forward to the neighbor closest to the destination ⓒ KAIST SE LAB 2008

  5. Introduction(3/6) • Some protocols have degraded performance • Quad-tree type data storage hierarchy • Data is hashed uniformly to the quads ⓒ KAIST SE LAB 2008

  6. Introduction(4/6) Quad-Tree Type Hierarchy ⓒ KAIST SE LAB 2008

  7. Introduction(5/6) Place base stations and avoid traffic bottleneck • Motivation • Global geometric features affect many aspects of sensor networks • Affect system performance • Affect network design ⓒ KAIST SE LAB 2008

  8. Introduction(6/6) • Research goal : Shape segmentation • Segment the irregular field into “nice” pieces • Each piece has no holes, and has a relatively nice shape • Apply existing protocols inside each piece • Existing protocols are reusable ⓒ KAIST SE LAB 2008

  9. Background(1/4) 0 H(p1) H(s1) p1 s2 Local max s1 s3 0 Reference : flow complex [Dey, Giesen, Goswami, WADS’03] • Segmentation with Flow Complex • Flow complex in continuous domain • Distance function : h(x) = min{║x - p║2 : p on boundary} • Medial axis : a set of points with at least two closest points on the boundary ⓒ KAIST SE LAB 2008

  10. Background(2/4) 0 H(p1) p1 s2 Local max s1 p2 s3 0 Reference : flow complex [Dey, Giesen, Goswami, WADS’03] • Segmentation with Flow Complex(cont’d) • Flow complex in continuous domain • Flow direction: the direction that h(x) increases fastest • Sinks: local maximum, no flow direction ⓒ KAIST SE LAB 2008

  11. Background(3/4) 0 Naturally partition along narrow necks s2 Local max s1 s1 s3 0 Reference : flow complex [Dey, Giesen, Goswami, WADS’03] • Segmentation with Flow Complex(cont’d) • Flow complex in continuous domain • Flow direction: the direction that h(x) increases fastest • Sinks: local maximum, no flow direction • Segments : set of points flow to the same sink ⓒ KAIST SE LAB 2008

  12. Background(4/4) • Implementation Challenges • No global view, no centralized authority • No location, only connectivity information • Distances are approximated by hop count • Goal : A distributed and robust segmentation algorithm ⓒ KAIST SE LAB 2008

  13. Algorithm Outline ⓒ KAIST SE LAB 2008

  14. Segmentation Algorithm(1/7) • Step 1 : Compute the medial axis • Boundary nodes flood inward simultaneously • Nodes record : minimum hop count & closest intervals on the boundary • Medial axis : more than two closest intervals ⓒ KAIST SE LAB 2008

  15. Segmentation Algorithm(2/7) Too many segments! • Step 2 : Compute the flow • Flow direction : a pointer to a neighbor with a higher hop count from the same boundary • Prefer neighbor with the most symmetric interval • Sinks must be on the medial axis • Nodes are classified into segments by their sinks ⓒ KAIST SE LAB 2008

  16. Segmentation Algorithm(3/7) • Step3 : Merge nearby sinks • Nearby sinks with similar hop count to the boundaries are merged (together with their segments) ⓒ KAIST SE LAB 2008

  17. Segmentation Algorithm(4/7) • Step3 : Merge nearby sinks(cont’d) • Nearby sinks with similar hop count to the boundaries are merged (together with their segments) • Segmentation granularity : |Hmax – Hmin| < t ⓒ KAIST SE LAB 2008

  18. Segmentation Algorithm(5/7) • Step4 : Final clean-up • Orphan nodes • Local maximum and nodes that flow into them • Merge orphan nodes with nearby segments ⓒ KAIST SE LAB 2008

  19. Segmentation Algorithm(6/7) Final result ⓒ KAIST SE LAB 2008

  20. Segmentation Algorithm(7/7) Simulation Results on Segmentation ⓒ KAIST SE LAB 2008

  21. Application(1/2) storage load storage load before segmentation after segmentation • Distributed index for multi-dimensional data(DIM) • Suffer from a complex shape sensor field • Shape segmentation can reduce load and communication cost ⓒ KAIST SE LAB 2008

  22. Application(2/2) • Random sampling • Shape segmentation can reduce the number of trials and cost ⓒ KAIST SE LAB 2008

  23. Conclusion • Contribution • A unified approach handling complex shape in sensor networks • A good example to extract high-level geometry from connectivity information • Network self-organizes by local operations • Future work • Find the definition to choose appropriate segmentation • It depends on application ⓒ KAIST SE LAB 2008

  24. Question ? ⓒ KAIST SE LAB 2008

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