Paper Info
Title
Optimizing Computation Offloading in Satellite-UAV-Served 6G IoT: A Deep Learning Approach
Abstract
Satellite networks can provide Internet of Things (IoT) devices in remote areas with seamless coverage and downlink multicast transmissions. However, the large transmission latency, serious path loss, as well as the energy and resource constraints of IoT terminals challenge the stringent service requirements for throughput and latency in the 6G era. To address these problems, technologies including space-air-ground integrated networks (SAGINs), machine learning, edge computing, and energy harvesting are highly expected in 6G IoT. In this article, we consider the unmanned aerial vehicles (UAVs) and satellites to offer wireless-powered IoT devices edge computing and cloud computing services, respectively. To accelerate the communications, Terahertz frequency bands are utilized for communications between UAVs and IoT devices. Since the tasks generated by terrestrial IoT devices can be conducted locally, offloaded to the UAV-based edge servers or remote cloud servers through satellites, we focus on the computation offloading problem and consider deep learning techniques to optimize the task success rate considering the energy dynamics and channel conditions. A deep-learning-based offloading policy optimization strategy is given where the long short-term memory model is considered to address the dynamics of energy harvesting performance. Through the theoretical explanation and performance analysis, we discover the importance of emerging technologies including SAGIN, energy harvesting, and artificial intelligence techniques for 6G IoT.
Year
DOI
Venue
2021
10.1109/MNET.011.2100097
IEEE Network
Keywords
DocType
Volume
satellite-UAV-served 6G IoT,deep learning approach,satellite networks,Things devices,remote areas,seamless coverage,downlink multicast transmissions,serious path loss,resource constraints,IoT terminals,stringent service requirements,SAGIN,machine learning,unmanned aerial vehicles,UAVs,wireless-powered IoT devices edge computing,cloud computing services,Terahertz frequency bands,terrestrial IoT devices,UAV-based edge servers,remote cloud servers,satellites,computation offloading problem,deep learning techniques,energy dynamics,offloading policy optimization strategy,energy harvesting performance
Journal
35
Issue
ISSN
Citations 
4
0890-8044
11
PageRank 
References 
Authors
0.49
0
4
Name
Order
Citations
PageRank
Bomin Mao126513.95
Fengxiao Tang225311.24
Yuichi Kawamoto3110.49
Nei Kato43982263.66