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Sensor network troubleshooting is a notoriously difficult task, further exacerbated by resource constraints, unreliable components, unpredictable natural phenomena, and experimental programming paradigms. This paper presents SNTS (Sensor Network Troubleshooting Suite), a tool that performs automated failure diagnosis in sensor networks. SNTS can be used to monitor network conditions using simple visualization...
Software bugs in distributed systems are notoriously hard to find due to the large number of components involved and the non-determinism introduced by race conditions between messages. This paper introduces Pop Mine, a tool for diagnosing corner-case bugs by finding the minimal causal directed acyclic graph (DAG) of events, spanning multiple processes, which captures a bug-triggering condition. Being...
This demonstration presents a tool, called DustDoctor, for troubleshooting sensor data fusion systems in which data is combined from multiple heterogeneous sources to compute actionable information. Application examples include target detection, critical infrastructure monitoring, and participatory sensing. In such systems, the correctness of end results may become compromised for a variety of possible...
When information sources are unreliable, information networks have been used in data mining literature to uncover facts from large numbers of complex relations between noisy variables. The approach relies on topology analysis of graphs, where nodes represent pieces of (unreliable) information and links represent abstract relations. Such topology analysis was often empirically shown to be quite powerful...
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