A Distributed Stream Data Processing Platform Design and Implementation in Smart Cities

Abstract

The existence of bounded data and unbounded data gives a great challenge for data processing in smart cities. The wide application of the internet of things (IoT) makes the data amount rapidly increase. This leads to further raise the requirement for data processing in smart cities, especially the demand for low latency and abundant data in real-time video services. To solve this problem, a Flink based framework with smart city adaption is proposed. A mathematical model for data processing in smart cities is formulated. Through this model's solution, the path with the minimum resource occupancy ratio (ROR) is obtained. The superiority and feasibility of our work are validated via numerical simulation and prototype implementation, respectively.