Hierarchical Edge-Cloud Task Offloading in NTN for Remote Healthcare

TL;DR

This paper proposes a hierarchical edge-cloud system using HAPS and LEO satellites for remote healthcare, optimizing task offloading and resource allocation to improve efficiency in IoMT environments.

Contribution

It introduces a novel hierarchical offloading framework combining HAPS and LEO satellites for remote healthcare, with optimized policies for bandwidth and task management.

Findings

Optimal per-task costs are derived for each tier.

Bandwidth allocation strategies are formulated for efficiency.

The system enhances remote healthcare task processing.

Abstract

In this work, we study a hierarchical non-terrestrial network as an edge-cloud platform for remote computing of tasks generated by remote ad-hoc healthcare facility deployments, or internet of medical things (IoMT) devices. We consider a high altitude platform station (HAPS) to provide local multiaccess edge server (MEC) services to a set of remote ground medical devices, and a low-earth orbit (LEO) satellite, serving as a bridge to a remote cloud computing server through a ground gateway (GW), providing a large amount of computing resources to the HAPS. In this hierarchical system, the HAPS and the cloud server charges the ground users and the HAPS for the use of the spectrum and the computing of their tasks respectively. Each tier seeks to maximize their own utility in a selfish manner. To encourage the prompt computation of the tasks, a local delay cost is assumed. We formulate the optimal per-task cost at each tier that influences the corresponding offloading policies, and find the corresponding optimal bandwidth allocation.