TY - GEN
T1 - DustDoctor
T2 - 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'11
AU - Khan, Mohammad Maifi Hasan
AU - Ahmadi, Hossein
AU - Dogan, Gulustan
AU - Govindan, Kannan
AU - Ganti, Raghu
AU - Brown, Theodore
AU - Han, Jiawei
AU - Mohapatra, Prasant
AU - Abdelzaher, Tarek
PY - 2011
Y1 - 2011
N2 - 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 reasons, such as node malfunction, bugs, environmental conditions unfavorable to certain sensors, or assumption mismatches (such as use of incompatible units on different nodes of the same distributed computation). DustDoctor adapts algorithms borrowed from previous discriminative mining literature to analyze data fusion flow graphs, called provenance graphs, and isolate sources and conditions correlated with anomalous results. This information is subsequently used to isolate malfunctioning components or filter out erroneous reports. We demonstrate our approach on MicaZ motes, running a simple data collection application, where users are allowed to inject a variety of different emulated faults, leaving it to DustDoctor to find and isolate them to prevent contamination of fusion results.
AB - 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 reasons, such as node malfunction, bugs, environmental conditions unfavorable to certain sensors, or assumption mismatches (such as use of incompatible units on different nodes of the same distributed computation). DustDoctor adapts algorithms borrowed from previous discriminative mining literature to analyze data fusion flow graphs, called provenance graphs, and isolate sources and conditions correlated with anomalous results. This information is subsequently used to isolate malfunctioning components or filter out erroneous reports. We demonstrate our approach on MicaZ motes, running a simple data collection application, where users are allowed to inject a variety of different emulated faults, leaving it to DustDoctor to find and isolate them to prevent contamination of fusion results.
KW - Data fusion
KW - Multi-sensor fusion
KW - Quality of information
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=79959315151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79959315151&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:79959315151
SN - 9781612848549
T3 - Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'11
SP - 127
EP - 128
BT - Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN'11
Y2 - 12 April 2011 through 14 April 2011
ER -