Software-Defined Edge Computing: A New Architecture Paradigm to Support IoT Data Analysis
This addresses the problem of efficient IoT data analysis for applications with strict communication needs, but it appears incremental as it builds on existing trends in edge computing.
The paper tackles the challenge of processing massive IoT data with specific latency and bandwidth requirements by proposing software-defined edge computing as a new architecture paradigm, and experimental results on data anomaly detection and ECG diagnosis show it is effective and feasible.
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.