JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

TL;DR

This paper proposes JC5A, a comprehensive optimization approach for aerial MEC-assisted ICPS that minimizes service delay by jointly optimizing offloading, caching, resource allocation, and UAV trajectory, addressing resource constraints and delay requirements.

Contribution

Introduces JC5A, a novel joint optimization framework for aerial MEC-assisted ICPS, with proven convergence and polynomial complexity, to effectively minimize service delay.

Findings

JC5A outperforms benchmark algorithms in reducing system delay.

The proposed methods ensure convergence and manageable complexity.

Simulation results validate the effectiveness of JC5A in practical scenarios.

Abstract

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms.