Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

Abstract

Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.