Intelligent Data-Intensive loT: A Survey

The loT paradigm proposes to connect entities intelligently with massive heterogeneous nature, which forms an ocean of devices and data whose complexity and volume are incremental with time. Different from the general big data or loT, the data-intensive feature of loT introduces several specific challenges, such as circumstance dynamicity and uncertainties. Hence, intelligence techniques are needed in solving the problems brought by the data intensity. Until recent, there are many different views to handle loT data and different intelligence enablers for loT, with different contributions and different targets. However, there are still some issues have not been considered. This paper will provide a fresh survey study on the data-intensive loT issue. Besides that, we conclude some shadow issues that have not been emphasized, which are interesting for the future. We propose also an extended big data model for intelligent data-intensive loT to tackle the challenges.