LAUSR

A service subgraph helps Internet-of-Things devices access resources in a dynamic Internet-of-Things device network. We propose a service subgraph generation method for Internet-of-Things device networks. Service subgraph generation aims to find more capable neighboring Internet-of-Things devices for service provisioning. We apply a line graph structure for an adequate representation of device resources. The line graph structure effectively represents the resources in the generated service subgraph. A general node classification problem constituting the generated service subgraph identifies the appropriate resource binding for service provisioning. A node in the service subgraph corresponds to a unique relationship between devices. Service provisioning is guaranteed by reinforcement learning based on the resource binding identified by node classification. The proposed line graph structure and resource binding significantly enhance the traditional intelligent resource allocation method. In addition, the proposed scheme can effectively attain service subgraphs with very low computational complexity. The proposed generative service provisioning generally has a significantly lower occupation probability than the swarm intelligence-based algorithm. The average value of the occupation probability is 0.49 with the proposed method. It is 0.12 lower than that of swarm intelligence-based algorithm.