Resilient Self-Calibration in Distribut...

Today, camera networks are pervasively used in

smart environments such as intelligent homes, industrial automa-

tion or surveillance. These applications often require cameras to

be aware of their spatial neighbors or even to operate on a

common ground plane. A major concern in the use of sensor

networks in general is their robustness and reliability even in

the presence of attackers.

This paper addresses the challenge of detecting malicious

nodes during the calibration phase of camera networks. Such

a resilient calibration enables robust and reliable localization

results and the elimination of attackers right after the network

deployment. Specifically, we consider the problem of identifying

subverted nodes which manipulate calibration data and can not

be detected by standard cryptographic methods. The experiments

in our network show that our self-calibration algorithm enables

location-unknown cameras to successfully detect malicious nodes

while autonomously calibrating the network.