Understanding the behavior of deployed sensor networks is difficult as they become more sophisticated and larger in scale. Much of the difficulty comes from the lack of tools to provide a global view on the network dynamics. This paper describes LiveNet, a set of tools and techniques for reconstructing complex dynamics of live sensor network deployments. LiveNet is based on the use of passive sniffers co-deployed with the network. We address several challenges: merging multiple sniffer traces, determining coverage of sniffers, inference of missing information for path reconstruction and high-level analyses with application-specific knowledge. To validate LiveNet's accuracy, we conduct controlled experiments on an indoor testbed. Finally, we present data from a real deployment using LiveNet. The results show that LiveNet is able to to reconstruct network topology, bandwidth usage, routing paths, identify hot-spot nodes, and disambiguate failures observed at application level without instrumenting application code.

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Public trust in government depends largely on the belief that institutions are fair and respond to the will of the governed. We expand on past research on the relationship between public opinion and state courts by studying how selection methods for both state and local courts influence popular attitudes about the judicial branch. Employing individual-level survey data on the responsiveness and fairness of state supreme courts and local trial courts, we find that respondents in states using elections to choose judges for state courts believe the judicial system is fairer. Further, the use of non-partisan elections for local trial courts has a positive effect on public evaluations of judicial fairness. However, views on judicial responsiveness are unaffected by means of selection at either the state or local level. Thus, nonpartisan or even partisan judicial elections do not have a negative effect on our measures of trust; indeed, when elections do have an effect, it is a positive one.

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