Priority-based Joint Perceptual Task Allocation Method in IoMT

Due to the heterogeneity of hospital workstations (i.e., edge devices) and the wide array of medical tasks, irrational task allocation may result in prolonged overall completion times. To avoid the failure of task execution due to the long waiting time in the internet of medical things(IoMT) scenario, a joint awareness task allocation method based on priority is proposed in this paper. In the method, a cluster network model is constructed by jointly perceiving task flow and node flow. The nodes of the cluster are assigned different capability levels and priorities by the cluster head manager, and the priority-based allocation strategy is optimized by the optimization model to ensure that tasks are assigned to nodes with lower capability levels as fairly as possible. This approach optimally determines whether a task is performed on a hospital workstation (HW) or in the cloud, so as to minimize the total task completion time and bandwidth cost. Once the calculation task is complete, the results will be immediately sent to the doctor so that the doctor can make a quick decision. Simulation results show that, compared with the traditional solution, the task completion time of the proposed method can be shorted by 37% on average.