FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897317974> ?p ?o ?g. }

• W2897317974 endingPage "8" @default.
• W2897317974 startingPage "1" @default.
• W2897317974 abstract "Because the existing approaches for diagnosing sensor networks lead to low precision and high complexity, a new fault detection mechanism based on support vector regression and neighbor coordination is proposed in this work. According to the redundant information about meteorological elements collected by a multisensor, a fault prediction model is built using a support vector regression algorithm, and it achieves residual sequences. Then, the node status is identified by mutual testing among reliable neighbor nodes. Simulations show that when the sensor fault probability in wireless sensor networks is 40%, the detection accuracy of the proposed algorithm is over 87%, and the false alarm ratio is below 7%. The detection accuracy is increased by up to 13%, in contrast to other algorithms. This algorithm not only reduces the communication to sensor nodes but also has a high detection accuracy and a low false alarm ratio. The proposed algorithm is suitable for fault detection in meteorological sensor networks with low node densities and high failure ratios." @default.
• W2897317974 created "2018-10-26" @default.
• W2897317974 creator A5007379696 @default.
• W2897317974 creator A5020074265 @default.
• W2897317974 creator A5056469519 @default.
• W2897317974 creator A5057134338 @default.
• W2897317974 creator A5082559201 @default.
• W2897317974 date "2018-10-21" @default.
• W2897317974 modified "2023-10-17" @default.
• W2897317974 title "Distributed Fault Detection for Wireless Sensor Networks Based on Support Vector Regression" @default.
• W2897317974 cites W1986841165 @default.
• W2897317974 cites W1997229237 @default.
• W2897317974 cites W2013100873 @default.
• W2897317974 cites W2050003337 @default.
• W2897317974 cites W2052732436 @default.
• W2897317974 cites W2054645302 @default.
• W2897317974 cites W2091674427 @default.
• W2897317974 cites W2099335953 @default.
• W2897317974 cites W2100294832 @default.
• W2897317974 cites W2109092038 @default.
• W2897317974 cites W2138365125 @default.
• W2897317974 cites W2140095548 @default.
• W2897317974 cites W2151764894 @default.
• W2897317974 cites W2169346729 @default.
• W2897317974 cites W2296377540 @default.
• W2897317974 cites W2296872963 @default.
• W2897317974 cites W2320517083 @default.
• W2897317974 cites W2345218633 @default.
• W2897317974 cites W2416084035 @default.
• W2897317974 cites W2429289022 @default.
• W2897317974 cites W2464719119 @default.
• W2897317974 cites W2548146996 @default.
• W2897317974 cites W2579542163 @default.
• W2897317974 cites W2582181923 @default.
• W2897317974 cites W2592236939 @default.
• W2897317974 cites W2610318103 @default.
• W2897317974 cites W2695737732 @default.
• W2897317974 cites W2734811486 @default.
• W2897317974 cites W2741060509 @default.
• W2897317974 cites W2754384272 @default.
• W2897317974 cites W2790298521 @default.
• W2897317974 cites W2801291122 @default.
• W2897317974 cites W2964101532 @default.
• W2897317974 cites W914875445 @default.
• W2897317974 doi "https://doi.org/10.1155/2018/4349795" @default.
• W2897317974 hasPublicationYear "2018" @default.
• W2897317974 type Work @default.
• W2897317974 sameAs 2897317974 @default.
• W2897317974 citedByCount "24" @default.
• W2897317974 countsByYear W28973179742019 @default.
• W2897317974 countsByYear W28973179742020 @default.
• W2897317974 countsByYear W28973179742021 @default.
• W2897317974 countsByYear W28973179742022 @default.
• W2897317974 countsByYear W28973179742023 @default.
• W2897317974 crossrefType "journal-article" @default.
• W2897317974 hasAuthorship W2897317974A5007379696 @default.
• W2897317974 hasAuthorship W2897317974A5020074265 @default.
• W2897317974 hasAuthorship W2897317974A5056469519 @default.
• W2897317974 hasAuthorship W2897317974A5057134338 @default.
• W2897317974 hasAuthorship W2897317974A5082559201 @default.
• W2897317974 hasBestOaLocation W28973179741 @default.
• W2897317974 hasConcept C10000559 @default.
• W2897317974 hasConcept C108037233 @default.
• W2897317974 hasConcept C111185680 @default.
• W2897317974 hasConcept C11413529 @default.
• W2897317974 hasConcept C12267149 @default.
• W2897317974 hasConcept C124101348 @default.
• W2897317974 hasConcept C127313418 @default.
• W2897317974 hasConcept C127413603 @default.
• W2897317974 hasConcept C152745839 @default.
• W2897317974 hasConcept C154945302 @default.
• W2897317974 hasConcept C155512373 @default.
• W2897317974 hasConcept C165205528 @default.
• W2897317974 hasConcept C172707124 @default.
• W2897317974 hasConcept C175551986 @default.
• W2897317974 hasConcept C24590314 @default.
• W2897317974 hasConcept C2776836416 @default.
• W2897317974 hasConcept C31258907 @default.
• W2897317974 hasConcept C41008148 @default.
• W2897317974 hasConcept C41971633 @default.
• W2897317974 hasConcept C555944384 @default.
• W2897317974 hasConcept C62611344 @default.
• W2897317974 hasConcept C66938386 @default.
• W2897317974 hasConcept C76155785 @default.
• W2897317974 hasConcept C77052588 @default.
• W2897317974 hasConcept C79403827 @default.
• W2897317974 hasConceptScore W2897317974C10000559 @default.
• W2897317974 hasConceptScore W2897317974C108037233 @default.
• W2897317974 hasConceptScore W2897317974C111185680 @default.
• W2897317974 hasConceptScore W2897317974C11413529 @default.
• W2897317974 hasConceptScore W2897317974C12267149 @default.
• W2897317974 hasConceptScore W2897317974C124101348 @default.
• W2897317974 hasConceptScore W2897317974C127313418 @default.
• W2897317974 hasConceptScore W2897317974C127413603 @default.
• W2897317974 hasConceptScore W2897317974C152745839 @default.
• W2897317974 hasConceptScore W2897317974C154945302 @default.
• W2897317974 hasConceptScore W2897317974C155512373 @default.
• W2897317974 hasConceptScore W2897317974C165205528 @default.

• next