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Sensor failure detection and recovery mechanism based on support vector and genetic algorithm
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 th...
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| creator | Jiehui Zhu Yang Yang Xuesong Qiu Zhipeng Gao |
| description | 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. |
| doi_str_mv | 10.1109/APNOMS.2014.6996565 |
| format | conference_proceeding |
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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.</description><identifier>EISBN: 9784885522888</identifier><identifier>EISBN: 4885522889</identifier><identifier>DOI: 10.1109/APNOMS.2014.6996565</identifier><language>eng</language><subject>Biological cells ; Clustering algorithms ; credibility level ; Fault detection ; fault recovery ; genetic algorithm ; Prediction algorithms ; Routing ; Support vector machines ; support vector regression ; Wireless sensor networks</subject><ispartof>The 16th Asia-Pacific Network Operations and Management Symposium, 2014, p.1-4</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6996565$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6996565$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jiehui Zhu</creatorcontrib><creatorcontrib>Yang Yang</creatorcontrib><creatorcontrib>Xuesong Qiu</creatorcontrib><creatorcontrib>Zhipeng Gao</creatorcontrib><title>Sensor failure detection and recovery mechanism based on support vector and genetic algorithm</title><title>The 16th Asia-Pacific Network Operations and Management Symposium</title><addtitle>APNOMS</addtitle><description>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.</description><subject>Biological cells</subject><subject>Clustering algorithms</subject><subject>credibility level</subject><subject>Fault detection</subject><subject>fault recovery</subject><subject>genetic algorithm</subject><subject>Prediction algorithms</subject><subject>Routing</subject><subject>Support vector machines</subject><subject>support vector regression</subject><subject>Wireless sensor networks</subject><isbn>9784885522888</isbn><isbn>4885522889</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkMtqAjEYhdNFocXOE7jJC8w0yWRyWYr0BloLui2SSf5oinMhiYJv3yl1dRbnO9_iIDSnpKKU6OfF1-dmva0YobwSWotGNHeo0FJxpZqGMaXUAypS-iGEUC2E0uIRfW-hT0PE3oTTOQJ2kMHmMPTY9A5HsMMF4hV3YI-mD6nDrUng8NSn8zgOMePLxE-CP_wAPeRgsTkdhhjysXtC996cEhS3nKHd68tu-V6uNm8fy8WqDJrksnW2Zk4K71pvFFHEKw_MCmqIYVIDU5ZaMLytqRRE2Zq33nIpauMl8Gk8Q_N_bQCA_RhDZ-J1f_ug_gU9E1V7</recordid><startdate>201409</startdate><enddate>201409</enddate><creator>Jiehui Zhu</creator><creator>Yang Yang</creator><creator>Xuesong Qiu</creator><creator>Zhipeng Gao</creator><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201409</creationdate><title>Sensor failure detection and recovery mechanism based on support vector and genetic algorithm</title><author>Jiehui Zhu ; Yang Yang ; Xuesong Qiu ; Zhipeng Gao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-bdc32d76fdbfa8080f8fe2c61a0a279e28c1cea4b317608c34bfc4763af7e4dc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Biological cells</topic><topic>Clustering algorithms</topic><topic>credibility level</topic><topic>Fault detection</topic><topic>fault recovery</topic><topic>genetic algorithm</topic><topic>Prediction algorithms</topic><topic>Routing</topic><topic>Support vector machines</topic><topic>support vector regression</topic><topic>Wireless sensor networks</topic><toplevel>online_resources</toplevel><creatorcontrib>Jiehui Zhu</creatorcontrib><creatorcontrib>Yang Yang</creatorcontrib><creatorcontrib>Xuesong Qiu</creatorcontrib><creatorcontrib>Zhipeng Gao</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Explore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jiehui Zhu</au><au>Yang Yang</au><au>Xuesong Qiu</au><au>Zhipeng Gao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Sensor failure detection and recovery mechanism based on support vector and genetic algorithm</atitle><btitle>The 16th Asia-Pacific Network Operations and Management Symposium</btitle><stitle>APNOMS</stitle><date>2014-09</date><risdate>2014</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><eisbn>9784885522888</eisbn><eisbn>4885522889</eisbn><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.</abstract><doi>10.1109/APNOMS.2014.6996565</doi><tpages>4</tpages></addata></record> |
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| identifier | EISBN: 9784885522888 |
| ispartof | The 16th Asia-Pacific Network Operations and Management Symposium, 2014, p.1-4 |
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| language | eng |
| recordid | cdi_ieee_primary_6996565 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological cells Clustering algorithms credibility level Fault detection fault recovery genetic algorithm Prediction algorithms Routing Support vector machines support vector regression Wireless sensor networks |
| title | Sensor failure detection and recovery mechanism based on support vector and genetic algorithm |
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