Learning to Hybrid Offload in Space-Air-Ground Integrated Mobile Edge Computing for IoT Networks

Xuhui Zhang1; Wenchao Liu2; Huijun Xing3; Zhenzhen Jin4; Weilin Zang5; Shuqiang Wang5; Yanyan Shen5; Liang Xue6

10.1109/CYBER59472.2023.10256477

2023

2379-7711

979-8-3503-1520-2

2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

836-841

11-14 July 2023

Qinhuangdao, China

Recently, space-air-ground integrated network (SAGIN) has garnered considerable interest from both academia and industry due to its broad-coverage and high-reliability features collaborated by low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground devices. In the meantime, the integration of SAGIN with other emerging communication technologies is promising for research and applications. Mobile edge computing (MEC) enables the resource limited devices, i.e., the Internet of things (IoTs) devices, to offload their data packet to the data processing center for computing. In this paper, a space-air-ground integrated MEC network is studied, where the UAV and satellite are capable for providing computing services to IoT devices. All IoT devices could split their data packet for local computing and offloading. The IoT devices can communicate with the UAV by active mode and/or passive mode through backscatter communication. The utility efficiency maximization problem that jointly considers the data volume, time latency, and energy consumption is formulated. As the problem is nonconvex and can not be addressed by conventional methods, a deep reinforcement learning based method is proposed to acquire the data offloading policy. Numerous numerical results confirm the effectiveness and robustness of the proposed method compared to other benchmark methods.

SAGIN Mobile edge computing Backscatter communication Data offloading Deep reinforcement learning

其他

EI

20234314959606

Antennas ; Backscattering ; Budget control ; Data handling ; Deep learning ; Earth (planet) ; Energy efficiency ; Energy utilization ; Internet of things ; Mobile edge computing ; Numerical methods ; Orbits ; Unmanned aerial vehicles (UAV)

Ergonomics and Human Factors Engineering:461.4 ; Energy Conservation:525.2 ; Energy Utilization:525.3 ; Aircraft, General:652.1 ; Data Communication, Equipment and Techniques:722.3 ; Digital Computers and Systems:722.4 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2 ; Artificial Intelligence:723.4 ; Numerical Methods:921.6

IEEE

1.Guangdong Provincial Key Laboratory of Future Networks of Intelligence, School of Science and Engineering (SSE), the Future Network of Intelligence Institute (FNii), the Chinese University of Hong Kong, Shenzhen Institute of Advanced Technology (SlAT), Chinese Academy of Sciences, Shenzhen, China
2.SIAT, Chinese Academy of Sciences, Southern University of Science and Technology (SUSTech), Shenzhen, China
3.SSE, the FNii, and the Guangdong Provincial Key Laboratory of Future Networks of Intelligence, the Chinese University of Hong Kong, Shenzhen, China
4.SUSTech, Shenzhen, China
5.SIAT, Chinese Academy of Sciences, Shenzhen, China
6.School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China

Xuhui Zhang,Wenchao Liu,Huijun Xing,et al. Learning to Hybrid Offload in Space-Air-Ground Integrated Mobile Edge Computing for IoT Networks[C],2023:836-841.