J. Hwang, T. He, Y. Kim Presented by Shan Gao

Introduction  Target the scenarios where attackers announce phantom nodes.  Phantom node  Fake their ranging information  Identify and filter out  A location map for individual nodes  A visual representation on the locations of neighbors of a node

 Prevent phantom nodes from generating consistent ranging claims to multiple honest nodes.  If the phantom nodes generate a set of inconsistent ranging claims, they can be detected.  Only distances to other neighboring nodes are allowed to be claimed, not the location information.

Idea  To prevent phantom nodes generating a set of fake we can:  Accepting any ranging claims, not location claims  Hiding the location information during the ranging phase.

Problem Definition  Nbr(v) neighbor of v and v  D the distance set  measured distance  calculated distance  A set of nodes is consistent, if they can be projected on the unique Euclidean plane, keeping the measured distances among themselves.

Approach  2 phases 1. Distance measurement phase  Each node measures the distances to its neighbors.  TOA, TDOA 2. Filtering phase  Each node projects its neighboring nodes to a virtual local plane to determine the largest consistent subset of nodes.  Eventually, each node establishes a local view without phantom nodes.  Useful in location-based routing and sensing coverage.

1. Distance measurement phase 1. Measures distance to each neighbor through a certain ranging method such as TDOA or TOA. 2. Announces the measured distances. 3. Collect neighbors’ announcement on the measured distances to their neighbors. 4. Compare collected data.  Prevent attack: round robin fashion announcement

2. Filtering phase 1. Each node v randomly picks up 2 neighbors to construct a coordinate system. 2. Use a graph G(V, E) to construct a consistent subset.  If, drop this edge.  The largest connected set V that contains node v is regarded as the largest consistent subset.  ε depends on the noise in the ranging measurement.  Repeat iter times. The cluster with the largest size is chosen as a final result.

Locations of nodes, node 6 is a phantom node. Computed plane from pivot 0, 5, 18 Computed plane from pivot 0, 6, 18

Simulation result

Distribution of number of nodes verified

Thanks Q&A?