Aerial-IRSs-Assisted Energy-Efficient Task Offloading and Computing

doi:10.1109/JIOT.2024.3371586

Aerial-IRSs-Assisted Energy-Efficient Task Offloading and Computing

2024-06-01

2

PRE

AI

DOI：10.1109/JIOT.2024.3371586

Original

Original request for help

Save

Abstract

En 中文

Timely and energy-efficient task offloading and computing can be challenging in mobile edge computing (MEC) networks when the communication links between devices and edge servers are unreliable. In this article, we apply multiple aerial intelligent reflective surfaces (AIRSs) to assist devices in offloading computing tasks to the edge server in a timely and reliable manner in the MEC network with poor offloading environments. To evaluate the timeliness of offloading and computing, we derive the evolution process of Age-of-Information (AoI) under the random arrival of the computing tasks. The association between devices and AIRSs, offloading order of computing tasks, design of IRS phase shift, and allocation of communication and computing resources are jointly optimized to minimize the average AoI and system energy consumption given computing requirements. To solve the formulated minimization problem, we propose an efficient problem-solving framework to cope with the challenge of variable coupling. First, we derive a closed-form optimal IRS phase shift to provide a reliable offloading environment. Then, we optimize the association between devices and AIRSs while reducing the offloading complexity and balancing the number of devices associated with each AIRS. Finally, we develop a low-complexity task offloading and resource allocation algorithm based on convex optimization to attain a good enough solution. Simulation results indicate the proposed solution outperforms benchmarks in timeliness and energy saving.

Keywords:

Task analysis

Resource management

Energy consumption

Servers

Internet of Things

Performance evaluation

Autonomous aerial vehicles

Aerial intelligent reflective surface (AIRS)

Age of Information (AoI)

energy consumption

mobile edge computing (MEC)

Aerial-IRSs-Assisted Energy-Efficient Task Offloading and Computing

Abstract

Journal

Organization