An edge computing-based network traffic measurement of the Internet of Things
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Abstract:
In the Internet of Things, edge computing can provide real-time performance of IoT computing and reduce the amount of data transmitted in the network. In order to satisfy the development of the IoT technology, the IoT network with software defined network (SDN) architecture was studied in this paper, and the method provided by SDN was used to measure the traffic in the network. Fine-grained traffic measurement can accurately describe traffic in the network, but it also consumes a lot of measurement overhead generated in the measurement process. To reduce the overhead and obtain approximate fine-grained measurements, an IoT network traffic measurement scheme based on edge computing was proposed. The new measurement architecture involved coarse-grained measurements and interpolation optimization. The random sampling method was used to measure coarse-grained network traffic through Open Flow protocol. Then, the coarse-grained network traffic was interpolated and restored, and the interpolation result was optimized by the multi-constraint optimization method to obtain the optimal fine-grained flow measurement result that satisfied the condition constraint. Finally, the feasibility and effectiveness of the proposed measurement method were verified by experiments.
