Sensor faults: Detection methods and prevalence in real-world datasets

ACM Transactions on Sensor Networks

Volumn 6, Issue 3, 2010, Pages

  Sharma, Abhishek B ^a   Golubchik, Leana ^b   Govindan, Ramesh ^a  

^a University of Southern California ^*  (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Data integrity; Fault detection; Fault prevalence; Sensor networks; Statistical techniques

Indexed keywords

APRIORI; CHOICE OF PARAMETERS; DATA INTEGRITY; DATA SETS; DETECTION METHODS; DOMAIN KNOWLEDGE; ESTIMATION METHODS; FALSE POSITIVE; FAULTY SENSOR; HEURISTIC RULES; LEARNING METHODS; LEARNING-BASED METHODS; LONG DURATION; ON-LINE FAULT DETECTION; REAL-WORLD; REAL-WORLD DATASETS; RULE-BASED METHOD; SENSOR BEHAVIOR; SENSOR CORRELATION; SENSOR FAULT; SENSOR MEASUREMENTS; SENSOR NETWORK MEASUREMENT; SENSOR READINGS; SHORT DURATIONS; STATISTICAL TECHNIQUES; TIME SERIES FORECASTING; TRAINING DATA; TRANSIENT FAULTS;

FAULT DETECTION; KNOWLEDGE MANAGEMENT; SENSOR NETWORKS; TIME SERIES; TIME SERIES ANALYSIS;

HEURISTIC METHODS;

EID: 77954025800     PISSN: 15504859     EISSN: 15504867     Source Type: Journal    
DOI: 10.1145/1754414.1754419     Document Type: Article

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