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Hierarchical Edge-Cloud Task Offloading in NTN for Remote Healthcare

Alejandro Flores, Danial Shafaie, Konstantinos Ntontin, Elli Kartsakli, Symeon Chatzinotas

TL;DR

This work addresses remote healthcare task offloading over a hierarchical non-terrestrial network comprising ground devices, a HAPS MEC, and a LEO-mediated MCC. It introduces a game-theoretic framework where each tier optimizes its own utility under a local delay cost, and develops a convex bandwidth allocation scheme with backward-induction pricing to determine per-task costs and offloading decisions. The key contributions include closed-form expressions for optimal HAPS spectrum sharing, a min-max fairness approach for GD-HAPS allocations, and an iterative, convergence-guaranteed procedure that achieves efficient edge-cloud distribution of three task types while respecting delay constraints. The proposed approach demonstrates how NTN resources can enable prompt, reliable remote healthcare processing in disaster or remote settings by balancing latency, cost, and resource availability across tiers.

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.

Hierarchical Edge-Cloud Task Offloading in NTN for Remote Healthcare

TL;DR

This work addresses remote healthcare task offloading over a hierarchical non-terrestrial network comprising ground devices, a HAPS MEC, and a LEO-mediated MCC. It introduces a game-theoretic framework where each tier optimizes its own utility under a local delay cost, and develops a convex bandwidth allocation scheme with backward-induction pricing to determine per-task costs and offloading decisions. The key contributions include closed-form expressions for optimal HAPS spectrum sharing, a min-max fairness approach for GD-HAPS allocations, and an iterative, convergence-guaranteed procedure that achieves efficient edge-cloud distribution of three task types while respecting delay constraints. The proposed approach demonstrates how NTN resources can enable prompt, reliable remote healthcare processing in disaster or remote settings by balancing latency, cost, and resource availability across tiers.

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.
Paper Structure (21 sections, 26 equations, 3 figures, 2 algorithms)

This paper contains 21 sections, 26 equations, 3 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Delay Model
  5. Transmission Delay
  6. Computing Delay
  7. Cost Model
  8. Cost for GDs
  9. Cost and Utility for MEC server
  10. Utility for Cloud Server
  11. Problem Formulation
  12. Proposed Solution
  13. Price Setting and Offloading
  14. Spectrum Allocation
  15. HAPS-GW
  16. ...and 6 more sections

Figures (3)

  • Figure 1: System Model
  • Figure 2: Per-tier cost over virtual delay cost $c_\tau$.
  • Figure 3: Computing location of tasks per task type.