  The rapid deployment of Internet of Things (IoT) applications leads to
massive data that need to be processed. These IoT applications have specific
communication requirements on latency and bandwidth, and present new features
on their generated data such as time-dependency. Therefore, it is desirable to
reshape the current IoT architectures by exploring their inherent nature of
communication and computing to support smart IoT data process and analysis. We
introduce in this paper features of IoT data, trends of IoT network
architectures, some problems in IoT data analysis, and their solutions.
Specifically, we view that software-defined edge computing is a promising
architecture to support the unique needs of IoT data analysis. We further
present an experiment on data anomaly detection in this architecture, and the
comparison between two architectures for ECG diagnosis. Results show that our
method is effective and feasible.

Networking and Internet Architecture;Machine Learning