Energy-aware tasks offloading based on DQN in medical mobile devices

Abstract

Offloading some tasks from the local device to the remote cloud is one of the important methods to overcome the drawbacks of the medical mobile device, such as the limitation in the execution time and energy supply. The challenges of offloading task is how to meet multiple requirement while keeping energy-saving. We classify tasks in the medical mobile device into two kinds: the first is the task that hopes to be executed as soon as possible, those tasks always have a deadline; the second is the task that can be executed anytime and always has no deadlines. Past work always neglects the energy consumption when the medical mobile device is charged. To the best of our knowledge, this paper is the first paper that focuses on the energy efficiency of charging from a power grid to a medical device during work. By considering the energy consumption in different locations, the energy efficiency during working and energy transmission, the available energy of and the battery, we propose a scheduling method based on DQN. Simulations show that our proposed method can reduce the number of un-completed tasks, while having a minimum value in the average execution time and energy consumption.