Digital Twin-Assisted Task Offloading in UAV-MEC Networks With Energy Harvesting for IoT Devices

Document Type

Article

Source of Publication

IEEE Internet of Things Journal

Publication Date

8-7-2024

Abstract

We investigate digital twin-assisted task offloading in unmanned aerial vehicle (UAV)–mobile edge computing (UAV-MEC) networks with energy harvesting. Digital twin technology leverages a real-time simulated environment to optimize UAV-MEC networks. Considering unpredictable MEC environments and low-power Internet of things (IoT) devices, we propose a digital twin-assisted task offloading scheme in UAV-MEC networks with energy harvesting. The goal is to minimize latency and maximize the number of associated IoT devices by optimizing UAV placement and IoT device association. The constraints on computing, caching, energy harvesting, latency and maximum number of IoT devices a UAV can serve are considered. To solve the formulated problem, we employ a branch-and-bound algorithm to obtain optimal results. We also solve the optimization problem using relaxed heuristic algorithm. In addition, we propose a difference of convex penalty-based algorithm to solve the problem with reduced computational complexity. This approach provide efficient alternatives to obtain near-optimal solution. Through extensive simulations, we demonstrate the effectiveness of the proposed algorithm and validate the benefits of leveraging digital twin technology in UAV-MEC networks with energy harvesting.

ISSN

2372-4662

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Volume

PP

Issue

99

First Page

1

Last Page

1

Disciplines

Engineering

Keywords

Digital twin, UAV-MEC networks, Energy harvesting, IoT devices, Task offloading

Indexed in Scopus

no

Open Access

no

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