  Distributed computing enables Internet of vehicle (IoV) services by
collaboratively utilizing the computing resources from the network edge and the
vehicles. However, the computing interruption issue caused by frequent edge
network handoffs, and a severe shortage of computing resources are two problems
in providing IoV services. High altitude platform station (HAPS) computing can
be a promising addition to existing distributed computing frameworks because of
its wide coverage and strong computational capabilities. In this regard, this
paper proposes an adaptive scheme in a new distributed computing framework that
involves HAPS computing to deal with the two problems of the IoV. Based on the
diverse demands of vehicles, network dynamics, and the time-sensitivity of
handoffs, the proposed scheme flexibly divides each task into three parts and
assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform
synchronous computing. The scheme also constrains the computing of tasks at
RSUs such that they are completed before handoffs to avoid the risk of
computing interruptions. On this basis, we formulate a delay minimization
problem that considers task-splitting ratio, transmit power, bandwidth
allocation, and computing resource allocation. To solve the problem, variable
replacement and successive convex approximation-based method are proposed. The
simulation results show that this scheme not only avoids the negative effects
caused by handoffs in a flexible manner, it also takes delay performance into
account and maintains the delay stability.
