Dahyun Kim, Muhammad Rusyadi Ramli, Jae-Min Lee, Dong‐Seong Kim
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Poster: SeamFarm -- Distributed Data Analytic for Precision Agriculture based on Seamless Computing
This work proposes a framework for distributed data analytic for precision agriculture based on seamless computing paradigm named SeamFarm. Generally, heterogeneous nodes deployed for precision agriculture where these nodes generated an extensive amount of data. Then machine learning can be used to analyze this data for precision agriculture. However, most of the IoT devices are resource-constrained devices, which results in poor performance while conducting a machine learning task. Thus, in SeamFarm, we consider distributing the data as well as the task to all available nodes. The results show that SeamFarm can meet all of the functional and non-functional requirements of distributed data analytic for precision agriculture. Moreover, it can obtain faster data analytic results.