A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Periodical |
| Magazine / Source | International Journal of Organizational and Collective Intelligence |
| MU Faculty or unit | |
| Citation | |
| Web | http://dx.doi.org/10.4018/IJOCI.2019040102 |
| Doi | http://dx.doi.org/10.4018/IJOCI.2019040102 |
| Keywords | Big Data; Internet of Things; Clustering Algorithm; Machine Learning; Mobile Networks |
| Description | Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this paper starts from reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this paper identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts. |
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