Hierarchical Edge-Cloud Task Offloading in NTN for Remote Healthcare

2026-02-19

AI Summary

This work addresses the challenges of efficiency and incentive alignment in Internet of Medical Things (IoMT) task offloading for telemedicine scenarios within non-terrestrial networks (NTNs). To this end, the authors propose a hierarchical edge–cloud collaborative architecture composed of High-Altitude Platform Stations (HAPS), Low Earth Orbit (LEO) satellites, and terrestrial gateways. For the first time, hierarchical game theory is introduced into NTN task offloading to model the selfish pricing behaviors of HAPS and cloud servers. The framework jointly optimizes latency-sensitive task offloading decisions and bandwidth allocation while respecting stringent delay constraints. By balancing the utility maximization of resource providers across all tiers, the proposed approach significantly reduces task latency costs and enhances both computational efficiency and economic feasibility of the overall system.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• task offloading
• non-terrestrial network
• remote healthcare
• edge-cloud computing
• resource allocation

Innovation

• Hierarchical Edge-Cloud Offloading
• Non-Terrestrial Network (NTN)
• High Altitude Platform Station (HAPS)
• Low Earth Orbit (LEO) Satellite
• Multi-access Edge Computing (MEC)