NDLI: Sensor failure detection and recovery mechanism based on support vector and genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Jiehui Zhu Yang Yang Xuesong Qiu Zhipeng Gao
Copyright Year	2014
Description	Author affiliation: State Key Lab. of Networking & Switching Technol., Beijing Univ. of Posts & Telecommun., Beijing, China (Jiehui Zhu; Yang Yang; Xuesong Qiu; Zhipeng Gao)
Abstract	The main role of wireless sensor networks is to collect environmental data. As the sensor nodes are vulnerable and work in unpredictable environments, sensors are possible to fail and return unexpected response. Therefore, fault detection and recovery are important in wireless sensor networks. In this paper, we propose a fault detection algorithm based on support vector regression, which predicts the measurements of sensor nodes by using historical data. Credit levels of sensor nodes will be determined by a contrast between predictions and actual measured values. In this paper we also propose a fault recovery algorithm according to the node credit levels combined with genetic algorithm. The simulation results demonstrate that the algorithms we propose work well in failure detection rate, fault recovery speed and energy consumption.
Sponsorship	IEICE ICM
Starting Page	1
Ending Page	4
File Size	459493
Page Count	4
File Format	PDF
ISBN	9784885522888
DOI	10.1109/APNOMS.2014.6996565
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-17
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Wireless sensor networks Fault detection Genetic algorithm Clustering algorithms Credibility level Prediction algorithms Routing Support vector regression Fault recovery Biological cells
Content Type	Text
Resource Type	Article

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