LAUSR

Detecting traces of patients with novel coronavirus pneumonia (COVID-19) is a prerequisite for avoiding the virus’s rapid spread. However, too much patient privacy data uploaded to the cloud centre will overwhelm the network and cause user information security not to be guaranteed. In this paper, we propose a personal prediction method for COVID-19 infections by perceiving the information of worn biosensors and monitoring equipment in a body area network (BAN). Edge computing and blockchain technology are introduced to solve the problems of user privacy protection and perceptual data transmission and storage. We first construct an edge body area network (EBAN) and characterize the edge blockchain cost’s maximization function by considering the bandwidth, storage space, and energy consumption constraints. Then we build a blockchain without redundant perception information and select effective transmission paths by using the edge blockchain construction efficiency maximization (EBCEM) algorithm. Finally, we use the network simulator (NS-2) to simulate the performance of the EBCEM algorithm and compare it with the excellent assignment game algorithm (AGA) in terms of the effective requester ratio (ERR), effective provider ratio (EPR), edge blockchain construction success ratio (EBCSR), and average storage usage ratio (ASUR) in the EBAN.