Hybrid NOMA-OMA Transmission Scheduling for Production Efficiency Maximization in Industrial Edge Computing Networks

• Published in: IEEE internet of things journal Vol. 11; no. 23; pp. 37493 - 37508
• Main Authors: Zhao, Yunzhi, Pei, Yanhua, Liu, Yong, Hou, Fen, Zhuang, Weihua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Assembly lines; Computation offloading; Decomposition; Delay requirement; Delays; Edge computing; Efficiency; hybrid transmission; Industrial applications; Industrial Internet of Things; Industrial Internet of Things (IIoT); Internet of Things; Maximization; Mobile computing; mobile edge computing (MEC); NOMA; Nonorthogonal multiple access; Optimization; Production; Production scheduling; Quality of service; Quality of service architectures; Resource management; Sensors; Task analysis; task priority; Task scheduling
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3433558

We consider a mobile edge computing (MEC) assisted Industrial Internet of Things (IIoT) network, where multiple assembly processing lines in a smart factory are equipped with sensing devices. They sense raw products, generate and offload computing tasks, and finally process the raw products based on the computing results. In this scenario, different positions of the processing machines lead to different priorities and diverse Quality-of-Service (QoS) requirements of tasks. Therefore, how to schedule tasks and allocate the network resources becomes a critical and challenging issue. In this study, we introduce a novel batch-based hybrid nonorthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission scheme. The selection between NOMA and OMA schemes is optimized based on the QoS requirements of tasks. Then, we formulate a production efficiency maximization problem with the objective of maximizing the speed of the assembly lines subject to the deadline constraints of offloading and computing procedures. To this end, a two-layer decomposition method is used to decompose the formulated problem into two subproblems. Furthermore, we utilize a bisection searching method to approximate the optimal solution, and propose an efficient method to determine the feasibility of the top-layer subproblem. Simulation results demonstrate the significant performance improvement of our proposed method. In specific, the production efficiency is enhanced by 525% in comparison with pure NOMA scheme.