Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Secure Task Offloading and Resource Allocation Design for Multi-Layer Non-Terrestrial Networks

Alejandro Flores, Isabella W. G. da Silva, Vu Nguyen Ha, Konstantinos Ntontin, Hien Quoc Ngo, Michail Matthaiou, Symeon Chatzinotas

TL;DR

This work considers a tag-based physical-layer authentication scheme to authenticate legitimate users, and formulates a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent.

Abstract

Remote and resource-constrained Internet-of-Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.

Secure Task Offloading and Resource Allocation Design for Multi-Layer Non-Terrestrial Networks

TL;DR

This work considers a tag-based physical-layer authentication scheme to authenticate legitimate users, and formulates a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent.

Abstract

Remote and resource-constrained Internet-of-Things (IoT) deployments often lack terrestrial connectivity for task offloading, motivating non-terrestrial networks (NTNs) with onboard multiaccess edge computing (MEC) capabilities. Nevertheless, in the presence of malicious actors, authentication needs to be performed to avoid non-authorized nodes from draining the computing resources of the NTN nodes. As a solution, we propose a four-layer MEC-enabled NTN with unmanned aerial vehicles (UAVs) acting as access nodes, a high altitude platform station (HAPS) acting as coordinator and authenticator, and a constellation of low-Earth orbit satellites (LEOSats) acting as remote MEC servers. We consider a tag-based physical-layer authentication (PLA) scheme to authenticate legitimate users, and formulate a joint task offloading decision and resource allocation for the admitted tasks, which is solved via block coordinate descent. Numerical results show that the PLA scheme is efficient and performs better than the benchmark schemes. We also demonstrate that the proposed scheme is robust against malicious attacks even under relaxed false-alarm constraints.
Paper Structure (13 sections, 1 theorem, 25 equations, 4 figures, 1 algorithm)

This paper contains 13 sections, 1 theorem, 25 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model - Joint Authentication, Offloading, and Resource Allocation Framework
  3. Authentication Framework
  4. Transmission Phase
  5. Authentication Phase
  6. Joint Offloading and Resource Allocation
  7. Problem Formulation
  8. Proposed Solution
  9. Offloading Decision
  10. Remote Computing Resource Allocation
  11. Performance Evaluation
  12. Conclusions
  13. Future Works

Key Result

Proposition 1

The closed-form expressions for the PFA, the PD, and the optimal decision threshold of the $i$th IoT device are given, respectively, by where $Q(\cdot)$ represents the Q-function, and $\sigma_{n_i}^2$ denotes the per-symbol variance of $\mathbf{n}_i$.

Figures (4)

  • Figure 1: Illustration of the considered system model.
  • Figure 2: Proportion of feasible tasks for varying $\tau_i^{\mathrm{max}}$.
  • Figure 3: Proportion of legitimate and malicious tasks admitted due to authentication versus the fixed PFA rate, $\mathsf{P}_{\mathrm{FA}}$, for different number of UAVs.
  • Figure 4: Maximum relative delay, $\eta$, with varying IoT transmit power, $p_i$. for different tag allocation power ,$\rho_{\text{t},i}^2$.

Theorems & Definitions (1)

  • Proposition 1