DocumentCode :
167078
Title :
Use of adaptive estimation as a predictor to evaluate sensor reading anomalies in Wireless Sensor Networks
Author :
Beheshti, Babak D.
Author_Institution :
Sch. of Eng. & Comput. Sci., New York Inst. of Technol., Westbury, NY, USA
fYear :
2014
fDate :
2-2 May 2014
Firstpage :
1
Lastpage :
6
Abstract :
Wireless Sensor Networks (WSNs) are deployed in the field to collect some physical data and report the data to a management entity. The very context of WSN usage implies autonomous operation. Consequently fault tolerant attributes are of high interest in WSNs, and therefore an active area of research. Healthy sensor nodes can occasionally report readings that are outside of the expected range. These out of range data, which are invalid and should be discarded, can be due to noise in the environment, a temporary power fluctuation, or any other transient malfunction.The question of how do we know if a sensor is reporting abnormal data, as opposed to the sensor being healthy and simply reporting accurate data that happens to differ in characteristics from the previous patterns is of particular interest in this paper. In this paper we present the underlying theory of our approach, the implementation of this algorithm, as well as simulation results that confirm auto-regressive models as a reliable signal behavior predictor.
Keywords :
adaptive estimation; autoregressive processes; fault tolerance; wireless sensor networks; WSN; adaptive estimation; autonomous operation; autoregressive model; fault tolerant; physical data collection; power fluctuation; sensor nodes; sensor reading anomaly evaluation; signal behavior predictor; transient malfunction; wireless sensor network; Least squares approximations; Mathematical model; Noise; Prediction algorithms; Time series analysis; Transient analysis; Wireless sensor networks; fault tolerance; wireless sensor networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Applications and Technology Conference (LISAT), 2014 IEEE Long Island
Conference_Location :
Farmingdale, NY
Type :
conf
DOI :
10.1109/LISAT.2014.6845214
Filename :
6845214
Link To Document :
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