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dc.contributor.authorGupta, Neeraj
dc.contributor.authorKhosravy, Mahdi
dc.contributor.authorPatel, Nilesh
dc.contributor.authorDey, Nilanjan
dc.contributor.authorGupta, Saurabh
dc.contributor.authorDarbari, Hemant
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.date2020-07
dc.date.accessioned2021-02-11T08:11:02Z
dc.date.available2021-02-11T08:11:02Z
dc.identifier.issn1573-7497
dc.identifier.urihttps://reunir.unir.net/handle/123456789/11004
dc.description.abstractIn the era of Internet of things (IoT), network Connection of an enormous number of agriculture machines and service centers is an expectation. However, it will be with a generation of massive volume of data, thus overwhelming the network traffic and storage system especially when manufacturers give maintenance service typically by various data analytic applications on the cloud. The situation is more complex in the context of low latency applications such as health monitoring of agriculture machines, although require emergency responses. Performing the computational intelligence on edge devices is one of the best approaches in developing green communications and managing the blast of network traffic. Due to the increasing usage of smartphone applications, the edge computation on the smartphone can highly assist the network traffic management. In connection with the mentioned point, in the context of exploiting the limited computation power of smartphones, the design of an AI-based data analytic technique is a challenging task. On the other hand, the users' need for economic technology makes it not to be easily pierced. This research work aims both targets by presenting a bi-level genetic algorithm approach of an optimized data analytic AI technique for monitoring the health of the agriculture vehicles which can be economically utilized on smartphone end-devices using the built-in microphones instead of expensive IoT sensors.es_ES
dc.language.isoenges_ES
dc.publisherApplied Scienceses_ES
dc.relation.ispartofseries;vol. 50, nº 11
dc.relation.urihttps://link.springer.com/article/10.1007/s10489-020-01744-x#citeases_ES
dc.rightsrestrictedAccesses_ES
dc.subjectGreen IoTes_ES
dc.subjectagricultural machinees_ES
dc.subjectartificial neural networkes_ES
dc.subjectevolutionary algorithmes_ES
dc.subjectedge computationes_ES
dc.subjecthealth-monitoringes_ES
dc.subjectJCRes_ES
dc.subjectScopuses_ES
dc.titleEconomic data analytic AI technique on IoT edge devices for health monitoring of agriculture machineses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1007/s10489-020-01744-x


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