Abstract:
With the fast development of the IoT, more and more attention has been paid to the acquisition of valuable information from real-time data streams. Nowadays, sampling-bas...Show MoreMetadata
Abstract:
With the fast development of the IoT, more and more attention has been paid to the acquisition of valuable information from real-time data streams. Nowadays, sampling-based approximate data analytics are properly the most widely used, which trade the output quality for efficiency. What's more, large bandwidth is need for transmitting large amounts of data is these systems. In fact, for some simple queries, we can use the computing ablility of edge nodes to get accurate and lossless results. This idea promotes the design of CalculIot, a real-time data flow system for edge computing and analysis. We construct edge computing nodes and transmission nodes to transfer the computing process originally in the cloud data center to the edge end, making full use of the computing resources of edge nodes, and at the same time, reducing the network transmission pressure and improving efficiency. To showcase the effectiveness of this algorithm, we implemented CalculIot and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. The results illustrated that for terminal query, our algorithm has higher accuracy and efficiency.
Date of Conference: 09-11 August 2019
Date Added to IEEE Xplore: 14 November 2019
ISBN Information: