Fault detection, correction, and tolerance for collaborative target localization in visual sensor networks

Abstract

Collaboration in visual sensor networks (VSNs) is essential not only to compensate for the limitations of each sensor node but also to tolerate inaccurate information generated by faulty sensors in the network. Fault tolerance in VSNs is more challenging than in conventional scalar sensor networks (SSNs) because of the directional sensing nature of cameras and the existence of visual occlusion. This paper focuses on the design of a collaborative target localization algorithm in VSNs that would not only accurately localize targets but also detect the faults in camera orientation, tolerate these errors and further correct them before they cascade. Targets are localized based on distributed camera nodes integrating the so-called certainty map generated at each node, that records the target non-existence information within the camera's field of view. Based on the locations of detected targets in the final certainty map, we then construct a generative image model in each camera that estimates the camera orientation, detect inaccuracies in camera orientations and correct them. Based on results obtained from both simulation and real experiments, we show that the proposed fault-tolerant method is effective in localization accuracy as well as fault detection and correction performance.