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

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.