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000074870 0247_ $$2doi$$a10.1109/ACCESS.2018.2825954
000074870 0248_ $$2sideral$$a107212
000074870 037__ $$aART-2018-107212
000074870 041__ $$aeng
000074870 100__ $$0(orcid)0000-0002-2726-6760$$aGarcia-Magarino, I.$$uUniversidad de Zaragoza
000074870 245__ $$aSurvivability Strategies for Emerging Wireless Networks with Data Mining Techniques: A Case Study with NetLogo and RapidMiner
000074870 260__ $$c2018
000074870 5060_ $$aAccess copy available to the general public$$fUnrestricted
000074870 5203_ $$aEmerging wireless networks have brought Internet and communications to more users and areas. Some of the most relevant emerging wireless technologies are Worldwide Interoperability for Microwave Access, Long-Term Evolution Advanced, and ad hoc and mesh networks. An open challenge is to ensure the reliability and robustness of these networks when individual components fail. The survivability and performance of these networks can be especially relevant when emergencies arise in rural areas, for example supporting communications during a medical emergency. This can be done by anticipating failures and finding alternative solutions. This paper proposes using big data analytics techniques, such as decision trees for detecting nodes that are likely to fail, and so avoid them when routing traffic. This can improve the survivability and performance of networks. The current approach is illustrated with an agent-based simulator of wireless networks developed with NetLogo and data mining processes designed with RapidMiner. According to the simulated experimentation, the current approach reduced the communication failures by 51.6% when incorporating rule induction for predicting the most reliable routes.
000074870 536__ $$9info:eu-repo/grantAgreement/ES/MEC/CAS17-00005$$9info:eu-repo/grantAgreement/ES/MEC/OAPEE-2013-1-CZ1-GRU06-14277$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2014-57028-R$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2017-84802-C2-1-P.$$9info:eu-repo/grantAgreement/ES/UZ/JIUZ-2017-TEC-03
000074870 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000074870 590__ $$a4.098$$b2018
000074870 591__ $$aCOMPUTER SCIENCE, INFORMATION SYSTEMS$$b23 / 155 = 0.148$$c2018$$dQ1$$eT1
000074870 591__ $$aTELECOMMUNICATIONS$$b19 / 88 = 0.216$$c2018$$dQ1$$eT1
000074870 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b52 / 265 = 0.196$$c2018$$dQ1$$eT1
000074870 592__ $$a0.609$$b2018
000074870 593__ $$aComputer Science (miscellaneous)$$c2018$$dQ1
000074870 593__ $$aMaterials Science (miscellaneous)$$c2018$$dQ1
000074870 593__ $$aEngineering (miscellaneous)$$c2018$$dQ1
000074870 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000074870 700__ $$aGray, G.
000074870 700__ $$0(orcid)0000-0002-4773-4904$$aLacuesta, R.$$uUniversidad de Zaragoza
000074870 700__ $$aLloret, J.
000074870 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000074870 773__ $$g6 (2018), 27958-27970$$pIEEE Access$$tIEEE Access$$x2169-3536
000074870 8564_ $$s8506575$$uhttps://zaguan.unizar.es/record/74870/files/texto_completo.pdf$$yVersión publicada
000074870 8564_ $$s116817$$uhttps://zaguan.unizar.es/record/74870/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000074870 909CO $$ooai:zaguan.unizar.es:74870$$particulos$$pdriver
000074870 951__ $$a2020-01-17-22:08:50
000074870 980__ $$aARTICLE