Details of Research Outputs

随着物联网和云计算的快速发展,衍生了一种新的网络结构——传感云.传感云是物联网和云计算结合的产物.物联网中的物理节点可以通过传感云平台虚拟成多个节点,为用户提供服务.然而,当底层物理传感器节点同时接收多个服务命令时,会出现一些服务冲突,即耦合问题.这种耦合问题可能导致服务的失败,并危及系统安全.为了解决这个问题,提出了一种基于边缘计算思想的扩展KM（Kuhn-Munkres）算法.边缘计算是一种新兴的计算模式,在物联网中得到越来越广泛的应用,特别是那些由于延迟等限制而无法有效利用云计算的应用.边缘计算作为云和物联网层的中间平台,可以提供调度方法.首先,边缘层对重复请求命令进行合并,减少向下传输的命令数量;其次,优先调用边缘层数据缓存队列里的数据;最后利用改进KM算法实现每一轮的最大匹配.理论分析和实验结果表明,提出的方法可以提高资源利用率,减小计算成本,接近最短的时间解决耦合问题. 

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. © 2020, Science Press. All right reserved.