IoT Big Data AnalyticsView this Special Issue
Editorial | Open Access
IoT Big Data Analytics
A successful Internet of things (IoT) environment requires standardization that contains interoperability, compatibility, reliability, and effectiveness of the operations on a global scale. The rapid growth of the IoT causes a sharp growth of data. Enormous amounts of networking sensors are continuously collecting and transmitting data to be stored and processed in the cloud. Such data can be environmental data, geographical data, astronomical data, logistic data, and so on. Mobile devices, transportation facilities, public facilities, and home appliances are the primary data acquisition equipment in IoT. The volume of such data will surpass the capacities of the IT architectures and infrastructure of existing enterprises and, due to real-time analysis character, will also greatly impact the computing capacity. Management of these increasingly growing data is a challenge for the community in general. Due to the generation of big data by IoT, the existing data-processing capacity of IoT is becoming ineffective, and it is imperative to incorporate big data technologies to promote the development of IoT. It is important to understand that the success of IoT lies upon the effective incorporation of big data analytics. The widespread deployment of IoT also gives a challenge to big data community to propose new techniques since both big data and IoT are interdependent themselves. On the one hand, the widespread deployment of IoT provides data both on quantity and on category, thus providing the opportunity for the application and development of big data; on the other hand, the incorporation of big data analytics in IoT simultaneously accelerates the research advances and business models of IoT.
This special issue provides some exciting papers in this area covering topics like the fault tolerant mechanism for big data applications using the cloud, hybrid parallel load-balancing algorithm for big data application, privacy-preserving service recommendation over sparse data, and genetic algorithm for the coevolution of meteorological data in the industrial Internet of things.
Conflicts of Interest
The editors declare that they have no conflicts of interest regarding the publication of this special issue.
Copyright © 2019 Salimur Choudhury et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.