Resilient Self-Calibration in Distributed Visual Sensor Networks

Today, camera networks are pervasively used in smart environments such as intelligent homes, industrial automation 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.