Akademik Veri Yönetim Sistemi
2021 International Symposium on Networks, Computers and Communications, ISNCC 2021, Dubai, Birleşik Arap Emirlikleri, 31 Ekim - 02 Kasım 2021
© 2021 IEEE.Recently, learning from continuously evolving streaming data attracts many researchers, especially when this data is inclined to change trends regularly (i.e. concept drift). Unluckily, conventional mining techniques and algorithms are proved inadequate to solve this problem, in which the model's performance degrades in stationary data, let alone in the case of data streams. Correspondingly, adjusting the model manually and constantly is ineffective, and with the current growth of the data size, it becomes impractical as well. However, automatic adaptive learning methods and algorithms could be a good solution, but they are seldom exploited in IoT business applications to address this issue. To that end, we aim to tackle the problem of concept drift occurs in time-series streaming data on IoT applications, in general, meteorology prediction, in specific. And taking into account the rapidly growing structure of fog-cloud computing, we attempt to leverage the powerful computation of the cloud layer as well as the closeness to IoT devices of the fog layer to design and implement a cooperative fog-cloud architecture in order to produce a faster and more accurate model. Our main objective is to obtain high performance and low latency, as these are the key requirements for real-time or nearly real-time data processing on IoT applications. The experimental findings have confirmed the study's hypothesis, where the adaptive model on the suggested cooperative fog-cloud architecture reduces both the error of prediction by 20% and the overall time for training the model by almost 41% compared to the baseline model.