High Altitude Platform Station (HAPS) Assisted Computing for Intelligent Transportation Systems

Abstract

High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing to improve intelligent transportation systems (ITS). HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities, which 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 propagation 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.