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Edge intelligence, a new paradigm to accelerate artificial intelligence (AI)applications by leveraging computing resources on the network edge, can be usedto improve intelligent transportation systems (ITS). However, due to physicallimitations and energy-supply constraints, the computing powers of edgeequipment are usually limited. High altitude platform station (HAPS) computingcan be considered as a promising extension of edge computing. HAPS is deployedin the stratosphere to provide wide coverage and strong computationalcapabilities. It is suitable to coordinate terrestrial resources and store thefundamental data associated with ITS-based applications. In this work, threecomputing layers,i.e., vehicles, terrestrial network edges, and HAPS, areintegrated to build a computation framework for ITS, where the HAPS datalibrary stores the fundamental data needed for the applications. In addition,the caching technique is introduced for network edges to store some of thefundamental data from the HAPS so that large propagation delays can be reduced.We aim to minimize the delay of the system by optimizing computation offloadingand caching decisions as well as bandwidth and computing resource allocations.The simulation results highlight the benefits of HAPS computing for mitigatingdelays and the significance of caching at network edges.

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