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

TL;DR

This work addresses delay-sensitive ICPS tasks in 6G-era IIoT by introducing a collaborative aerial MEC architecture where UAVs and an MBS share computing and caching resources. It formulates the SDMOP to minimize per-slot service delay under energy constraints and develops JC5A, a joint optimization framework that decomposes the problem into computation offloading/cache, resource allocation, and UAV trajectory control, solved via BSUMM and convex methods with proven convergence and polynomial complexity. The approach demonstrates superior average delay reduction and processing rate improvements over multiple baselines, while maintaining practical caching dynamics. The results highlight the practical potential of joint 3C optimization and UAV coordination to enable flexible, low-latency MEC services for dense ICPS deployments in dynamic industrial environments.

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.

Figures (6)

• Figure 1: The collaborative aerial MEC-assisted ICPS.
• Figure 2: The framework of J$\text{C}^5$A.
• Figure 3: Convergence of J$\text{C}^5$A.
• Figure 4: System performance with different average computation resources of UAVs.
• Figure 5: System performance with different numbers of ISDs.

Theorems & Definitions (12)

• Theorem 1
• proof
• Theorem 2
• proof
• Theorem 3
• proof
• Theorem 4
• proof
• Theorem 5
• proof