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With the rapid growth in the number of devices of the Internet of Things(IoT), the volume and types of stream data are rapidly increasing in the realworld. Unfortunately, the stream data has the characteristics of infinite andperiodic volatility in the real world, which cause problems with theinefficient stream processing tasks. In this study, we report our recentefforts on this issue, with a focus on simulating stream data. Firstly, weexplore the characteristics of the real-world stream data of the IoT, whichhelps us to understand the stream data in the real world. Secondly, thepipeline of simulating stream data is proposed, which can accurately andefficiently simulate the characteristics of the stream data to improveefficiency for specific tasks. Finally, we design and implement a novelframework that can simulate various stream data for related stream processingtasks. To verify the validity of the proposed framework, we apply thisframework to stream processing task running in the stream processing system.The experimental results reveal that the related stream processing task isaccelerated by at least 24 times using our proposed simulation framework withthe premise of ensuring volatility and trends of stream data.

A Framework for Simulating Real-world Stream Data of the Internet of Things