Hierarchical Edge-Cloud Task Offloading in NTN for Remote Healthcare

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.

💡 Research Summary

This paper investigates a three‑tier hierarchical non‑terrestrial network (NTN) architecture designed to support remote healthcare applications in areas where terrestrial connectivity is unavailable, such as disaster zones or conflict regions. The tiers consist of (i) ground medical IoMT devices that generate computational tasks, (ii) a high‑altitude platform station (HAPS) equipped with a multi‑access edge computing (MEC) server, and (iii) a low‑earth‑orbit (LEO) satellite together with a ground gateway (GW) that forwards tasks to a remote cloud data center.

Each ground device i creates a task ψ_i =