Dynamic spectrum optimization for Internet-of-Things with social distance model

In this paper, we address the spectrum efficiency enhancement for Internet of Things (IoT) by introducing the graph-theory-based spectrum optimization reuse. During the spectrum reuse, how to ascertain the interference range and user’s transmission status is critical. In this article, we model the interference range between IoT terminals by considering their interaction status which includes interaction frequency, duration and stability. The notion of interaction status is very similar to that of social distance among people in the context of pandemic control, which is a highly effective model for supporting contact tracing by analyzing the interaction frequency, duration and stability among people. Compared with other traditional dynamic spectrum optimization methods, introducing the concept of social distance can better evaluate the IoT user’s interference and transmission status from a novel perspective, then enhancing the optimal spectrum allocation. By modeling the social distances among IoT terminals, we estimate the desirable interference range for IoT devices which serves as the basis for graph-theory-oriented spectrum optimization. Together with the actual physical distances between IoT devices, they form the basis for optimizing spectrum reuse patterns. Graph theory is further utilized to complete the final spectrum optimization. Furthermore, comparison simulation tests are conducted to evaluate the performances of our proposed solution in network benefits and system capacity.Kindly check and confirm the inserted city “Singapore” is correctly identified.CorrectPlease provide author biographys and photos.The biographys and photos are correct.Kindly check and confirm the corresponding affiliation processed is correctly identified.Corresponding author is both at Aff1 and Aff2.