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Detecting Phantom Nodes in Wireless Sensor Networks

Detecting Phantom Nodes in Wireless Sensor Networks. Joengmin Hwang, Tian He, Yongdae Kim (ACM Infocom2007) Presenter : Justin. Main ideas. Two factors: Prevent the phantom nodes from generating consistent ranging (distance) claims to multiple honest nodes.

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Detecting Phantom Nodes in Wireless Sensor Networks

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  1. Detecting Phantom Nodes in Wireless Sensor Networks Joengmin Hwang, Tian He, Yongdae Kim (ACM Infocom2007) Presenter : Justin

  2. Main ideas • Two factors: • Prevent the phantom nodes from generating consistent ranging (distance) claims to multiple honest nodes. • Detect phantom nodes by the proposed speculative method

  3. Generating ranging claims • If the locations of neighboring nodes are known, it is easy to generate a fake location. • Without the location information of the neighboring nodes, it is hard for an attacker to generate a set of consistent ranging values (distances)

  4. Generating ranging claims C B D’ D A

  5. Generating ranging claims C D’C and D’B decrease D’A increase B D’ D A

  6. Generating ranging claims D’ A D B C

  7. Generating ranging claims D’C and D’B increase D’A decrease D’ A D B C

  8. The detailed approach • Definition: • A set of nodes is consistent, if they can be projected on the unique Euclidean plane (in 3-D case, Euclidean space), keeping the measured distances among themselves.

  9. The detailed approach • Problem: • Given a node set Nbr(v) that consists of a node v and its neighbors, and a distance set D that consists of the measured distance, denoted by Find the largest consistent subset of Nbr(v).

  10. The detailed approach • Two phases: • Distance Measurement Phase • Filtering Phase

  11. Distance Measurement • Node v measures distance to each neighbor i • Node v announces the measured distance • Node i announces its measured distance to its neighbor j, and v collects • For each collected distance, if , it is included in the filtering phase

  12. Filtering • Using a graph G(V,E) to construct a consistent subset. • The set V is used to contain the node v and its neighbors • The set E is used to keep the edges between two nodes when the distance information between them maintains consistency.

  13. Filtering • The local coordinate system L is determined by three nodes v, i,j with measured distance • Each node , calculating its location on L • Picking a pair of nodes , whose location on L are • Comparing the distance and ( which obtained in distance measurement phase ) • If , create edge e(i, j) in E • Choose the largest sizeof G(V,E)

  14. Filtering

  15. Filtering • If , create edge e(i, j) in E • Choose the largest sizeof G(V,E)

  16. Filtering • Node 6 is a phantom node

  17. Filtering

  18. Experiment results

  19. Experiment results

  20. Experiment results

  21. Conclusions • Pros • Presenting a way to exclude the phantom nodes by projecting each nodes into a local coordinate • The filtering operation is efficient • Cons • By using TDOA or TOA to measure distance, nodes need to be deployed at wide-space • It’s not suitable for small area application

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