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 ⓒ KAIST SE LAB 2008

Introduction(1/6) 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 irregular sensor field ! ⓒ KAIST SE LAB 2008

Introduction(2/6) Some protocols may fail Greedy forwarding : packets are greedily forward to the neighbor closest to the destination Dense uniform Sparse, non-uniform Works well May get stuck ⓒ KAIST SE LAB 2008

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

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

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

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

Background(1/4) 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 Local max H(s1) s1 s2 s3 p1 H(p1) Reference : flow complex [Dey, Giesen, Goswami, WADS’03] ⓒ KAIST SE LAB 2008

Background(2/4) 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 H(p1) p1 p2 Local max s2 s1 s3 Reference : flow complex [Dey, Giesen, Goswami, WADS’03] ⓒ KAIST SE LAB 2008

Background(3/4) 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 Local max s1 s3 Naturally partition along narrow necks s2 Reference : flow complex [Dey, Giesen, Goswami, WADS’03] ⓒ KAIST SE LAB 2008

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

Algorithm Outline ⓒ KAIST SE LAB 2008

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

Segmentation Algorithm(2/7) 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 Too many segments! ⓒ KAIST SE LAB 2008

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

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

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

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

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

Application(1/2) Distributed index for multi-dimensional data(DIM) Suffer from a complex shape sensor field Shape segmentation can reduce load and communication cost before segmentation after segmentation storage load Cost per event insertion cross corridor Fish Without shape segmentation 293.69 359.21 254.37 With shape segmentation 84.15 151.05 204.58 ⓒ KAIST SE LAB 2008

Application(2/2) Random sampling Shape segmentation can reduce the number of trials and cost No. of trials cross corridor Fish Without shape segmentation 168 149 136 With shape segmentation 112 115 123 Cost per sampling cross corridor Fish Without shape segmentation 477.84 511.95 361.80 With shape segmentation 102.49 182.32 238.47 ⓒ KAIST SE LAB 2008

Conclusion Contribution Future work 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

Question ? ⓒ KAIST SE LAB 2008