Affiliation:
1. School of Computer Science and Technology, Shaanxi Normal University, Xi’an 710119, China
2. Department of Computer Science & Technology, Xi’an Jiaotong University, Xi’an 710049, China
3. Xi’an Aeronautics Computing Technique Research Institute, AVIC, Xi’an 710068, China
Abstract
Mobile edge computing (MEC), specifically wireless powered mobile edge computing (WPMEC), can achieve superior real-time data analysis and intelligent processing. In WPMEC, different user nodes (UNs) harvest significantly different amounts of energy, which results in longer delays for lower-energy UNs when data are offloaded to MEC servers. This study involves quantifying the delays in energy harvesting and task offloading to edge servers in WPMEC via user cooperation. In this paper, a method for transferring the tasks that need to be offloaded to edge servers as quickly as possible is investigated. The problem is formulated as an optimization model to minimize the delay, including the time required for the energy harvesting and offloading tasks. Because the problem was non-deterministic polynomial hard (NP-hard), a delay-optimal approximation algorithm (DOPA) is proposed. Finally, with the training data generated based on the DOPA, a deep learning-based online offloading (DLOO) framework is designed for predicting the transmission power of each UN. After each UN’s transmission power is obtained, the original model is converted to a linear programming problem, which substantially reduces the computational complexity of the DOPA for solving the mixed-integer linear programming problem, especially in large-scale networks. The numerical results show that compared with the non-cooperation methods for WPMEC, the proposed algorithm significantly reduces the total delay. Additionally, in the delay optimization process for a scale of six UNs, the average computation time of the DLOO is only 0.2% that of the DOPA.
Funder
National Natural Science Foundation of China
Xi’an Science and Technology Plan Project
Natural Science Basic Research Program of Shaanxi
Reference24 articles.
1. The case for vm-based cloudlets in mobile computing;Satyanarayanan;IEEE Pervasive Comput.,2009
2. Bonomi, F., Milito, R., Zhu, J., and Addepalli, S. (2012, January 17). Fog computing and its role in the internet of things. Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, Helsinki, Finland.
3. Wireless networks with rf energy harvesting: A contemporary survey;Lu;IEEE Commun. Surv. Tutor.,2015
4. Energy-efficient resource allocation for mobile-edge computation of-floading;You;IEEE Trans. Wirel. Commun.,2017
5. Efficient multi-user computation offloading for mobile-edge cloud computing;Chen;IEEE/ACM Trans. Netw.,2016