Please use this identifier to cite or link to this item: http://ir.mu.ac.ke:8080/jspui/handle/123456789/7089
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKimutai, Gibson-
dc.contributor.authorNgenzi, Alexander-
dc.contributor.authorNgoga, Said Rutabayiro-
dc.contributor.authorFörster, Anna-
dc.date.accessioned2022-11-16T08:43:10Z-
dc.date.available2022-11-16T08:43:10Z-
dc.date.issued2021-
dc.identifier.urihttp://dx.doi.org/10.1109/GHTC53159.2021.9612420-
dc.identifier.urihttp://ir.mu.ac.ke:8080/jspui/handle/123456789/7089-
dc.description.abstractAgriculture contributes to the economies of many developing countries. Tea is the most popular crop in Kenya as it contributes majorly to her economy. Among the various stages of processing tea, fermentation is the most important as it determines the final quality of the processed tea. Presently, the process of monitoring is done manually by tea tasters by tasting, smelling, and touching tea which compromises the quality of tea. In this paper, a deep learner dubbed “TeaNet” is deployed in Edge and Fog environments for real-time monitoring of tea fermentation. We power the system using a Photovoltaic (PV) energy source to overcome the challenge of unreliable power supply from the grid. Further, the energy consumption of the solution is reduced by applying duty cycling, where idle components are designed to sleep. We used the Analysis of variance (ANOVA) and Post-hoc for data analysis. From the results, Edge registered the lowest latency compared to the Cloud and Fog environments. During deployment of the energy-optimized model, 50.6559Wh amount of energy was saved. This study recommends that the task offloading model proposed in this study be explored in offloading tasks in other fields.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectTea processingen_US
dc.subjectOff-loadingen_US
dc.titleOffloading an energy-efficient iot solution to the edge: a practical solution for developing countriesen_US
dc.typeArticleen_US
Appears in Collections:School of Biological and Physical Sciences

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.