Real-Time HAP-Assisted Vehicular Edge Computing for Rural Areas

TL;DR

This paper explores how High Altitude Platforms can serve as edge servers to support real-time vehicular edge computing in rural areas, enhancing connectivity and computational efficiency for IoT devices and ground vehicles.

Contribution

It introduces a model for HAP-assisted vehicular edge computing in rural settings and analyzes optimal offloading strategies to maximize real-time service probability.

Findings

Optimal offloading factor identified for maximum real-time service probability.

System modeled as queueing processes with Poisson task arrivals.

HAP-assisted VEC improves connectivity and computational efficiency in rural areas.

Abstract

Non-Terrestrial Networks (NTNs) are expected to be a key component of 6th generation (6G) networks to support broadband seamless Internet connectivity and expand the coverage even in rural and remote areas. In this context, High Altitude Platforms (HAPs) can act as edge servers to process computational tasks offloaded by energy-constrained terrestrial devices such as Internet of Things (IoT) sensors and ground vehicles (GVs). In this paper, we analyze the opportunity to support Vehicular Edge Computing (VEC) via HAP in a rural scenario where GVs can decide whether to process data onboard or offload them to a HAP. We characterize the system as a set of queues in which computational tasks arrive according to a Poisson arrival process. Then, we assess the optimal VEC offloading factor to maximize the probability of real-time service, given latency and computational capacity constraints.