<div>Numerous organizations around the world deploy sensor networks, especially visual sensor networks for various applications like monitoring traffic, security, and emergencies. With advances in computer vision technology, the potential application of these sensor networks has expanded. This has led to an increase in demand for deployment of large scale sensor networks.</div><div>Sensors in a large network have differences in location, position, hardware, etc. These differences lead to varying usefulness as they provide different quality of information. As an example, consider the cameras deployed by the Department of Transportation (DOT). We want to know whether the same traffic cameras could be used for monitoring the damage by a hurricane.</div><div>Presently, significant manual effort is required to identify useful sensors for different applications. There does not exist an automated system which determines the usefulness of the sensors based on the application. Previous methods on visual sensor networks focus on finding the dependability of sensors based on only the infrastructural and system issues like network congestion, battery failures, hardware failures, etc. These methods do not consider the quality of information from the sensor network. In this paper, we present an automated system which identifies the most useful sensors in a network for a given application. We evaluate our system on 2,500 real-time live sensors from four cities for traffic monitoring and people counting applications. We compare the result of our automated system with the manual score for each camera.</div><div>The results suggest that the proposed system reliably finds useful sensors and it output matches the manual scoring system. It also shows that a camera network deployed for a certain application can also be useful for another application.</div>