DETECTING PHANTOM NODES IN WIRELESS SENSOR NETWORKS

J. Hwang, T. He, Y. KimPresented 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 phases1. 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?