Research Area:  Machine Learning

Abstract:

Led by industrialization of smart cities, numerous interconnected mobile devices, and novel applications have emerged in the urban environment, providing great opportunities to realize industrial automation. In this context, autonomous driving is an attractive issue, which leverages large amounts of sensory information for smart navigation while posing intensive computation demands on resource constrained vehicles. Mobile edge computing (MEC) is a potential solution to alleviate the heavy burden on the devices. However, varying states of multiple edge servers as well as a variety of vehicular offloading modes make efficient task offloading a challenge. To cope with this challenge, we adopt a deep Q-learning approach for designing optimal offloading schemes, jointly considering selection of target server and determination of data transmission mode. Furthermore, we propose an efficient redundant offloading algorithm to improve task offloading reliability in the case of vehicular data transmission failure. We evaluate the proposed schemes based on real traffic data. Results indicate that our offloading schemes have great advantages in optimizing system utilities and improving offloading reliability.

Keywords:  
Deep Learning
Task Offloading
Mobile Edge Computing
Machine Learning

Author(s) Name:  Ke Zhang; Yongxu Zhu; Supeng Leng; Yejun He; Sabita Maharjan; Yan Zhang

Publisher name:  IEEE

DOI:  10.1109/JIOT.2019.2903191

Volume Information:   Volume: 6, Issue: 5, Page(s): 7635 - 7647

Paper Link:   https://ieeexplore.ieee.org/document/8660505