WiEdge: Edge Computing for Audio Sensing Applications With Accurate Wireless Link Prediction

Abstract

Audio sensing applications on embedded and mobile devices have recently enjoyed increasing popularity. Their performance can be significantly improved by edge computing which offloads computation-intensive tasks to edge servers through wireless links. The quality of wireless links is essential to offloading performance. However, existing edge computing solutions can hardly predict the link quality accurately and efficiently in a dynamic wireless environment, resulting in less optimal offloading decisions and unsatisfied user-perceived Quality of Experience (QoE). In this article, we present WiEdge, a distributed edge computing framework for audio sensing applications with accurate wireless link prediction. By combining cross-layer information extracted from recently received WiFi beacons, TCP-level statistics, and the past throughput observations, WiEdge can predict the throughput of wireless links accurately and efficiently in the near future. Based on the prediction, WiEdge makes optimal offloading decisions for QoE maximization. We formulate the offloading decision problem as a stochastic optimal control problem and propose an efficient solution based on model predictive control from the control-theoretic perspective. We implement WiEdge and evaluate its performance extensively in three representative real-world scenarios. Results show that WiEdge achieves high prediction accuracy and improves average normalized QoE by 2%, 11%, and 40% in three different scenarios, compared with state-of-the-art approaches.