ISSN 1000-1239 CN 11-1777/TP

### A Low-Coupling Method in Sensor-Cloud Systems Based on Edge Computing

Liang Yuzhu1, Mei Yaxin1, Yang Yi1, Ma Ying2, Jia Weijia3, Wang Tian1

1. 1(College of Computer Science and Technology, Huaqiao University, Xiamen, Fujian 361021);2(Key Laboratory of Data Mining and Intelligent Recommendation of Fujian Province University (Xiamen University of Technology), Xiamen, Fujian 361024);3(State Key Laboratory of Internet of Things for Smart City (University of Macau), Macau 999078)
• Online:2020-03-01
• Supported by:
This work was supported by the Open Fund of Key Laboratory of Data Mining and Intelligent Recommendation of Fujian Province University (DM201902), the Open Foundation of Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing (FZDX201908), the General Projects of Social Sciences in Fujian Province (FJ2018B038), the Natural Science Foundation of Fujian Province of China (2018J01092), and the Subsidized Project for Postgraduates’ Innovative Fund in Scientific Research of Huaqiao University (17013083005).

Abstract: The rapid development of the IoT and cloud computing has spawned a new network structure-sensor cloud. Sensor cloud is the combination of IoT and cloud computing. Physical sensor nodes in the IoT can be virtualized into multiple nodes through the sensor cloud platform to provide services to users. However, when one sensor node receives multiple service commands at the same time, some service conflicts occur, which named coupling problems. This coupling problem can lead to the failure of services and compromise system security. In order to solve this problem, this paper proposes an extended KM (Kuhn-Munkres) algorithm based on edge computing. Edge computing is an emerging computational paradigm, increasingly utilized in IoT applications, particularly those that cannot be served efficiently using cloud computing due to limitations such as latency. The edge computing platform acts as a middleware platform and provides the scheduling method. Firstly, the edge computing layer merges the similar commands to reduce the downward transmission commands. Secondly, the buffered data in the edge computing layer is scheduled. Finally, the extended KM algorithm is used to achieve the maximum matching of each round. The theoretical analysis and experimental results show that the proposed method can improve the utilization of resources, reduce the calculation cost, and solve the coupling problem in a minimum time.

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