Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers o...

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Bibliographic Details
Published in:IEEE transactions on wireless communications 2022-11, Vol.21 (11), p.9010-9024
Main Authors: Ren, Qiqi, Abbasi, Omid, Kurt, Gunes Karabulut, Yanikomeroglu, Halim, Chen, Jian
Format: Article
Language:English
Subjects:
Artificial intelligence
Autonomous aerial vehicles
Caching
Computation offloading
Delays
Edge computing
High altitude
High altitude platform station (HAPS)
Intelligent transportation systems
intelligent transportation systems (ITS)
Libraries
Resource allocation
Satellites
Task analysis
Transportation networks
Wireless communication
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Summary:Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2022.3171824