The industrial Internet of Things (IIoT), which integrates the key technologies of industrial communication, computing, and control, can implement flexible management and dynamic scheduling for manufacturing resources. To improve the edge intelligence of the IIoT, this article proposes a novel IIoT architecture with a hierarchical control structure in the mobile edge cloud (MEC). Massive remote radio heads (RRHs) are partitioned into several clusters, and each cluster is equipped with one or more servers for creating virtual machines (VMs) to execute the processing tasks of IIoT devices. The proposed IIoT architecture separates the control plane from the data plane based on software-defined networking (SDN). The hierarchical controllers improve the flexibility and intelligence of the control plane, while the RRHs and servers in the same cluster, forming an MEC-based radio access network (RAN) and supporting RAN function split, improve the scalability and cooperative gain of the data plane. A deep-learning technique is implemented in the MEC to further enhance the edge intelligence. In addition, we design two control schemes, one centralized and the other distributed, which provide a tradeoff between performance and overhead. Finally, aiming to minimize the system delay, we formulate a joint optimization problem of task scheduling, VM assignment, RRH allocation, and RAN function split as an example. To find solutions, a heuristic algorithm is proposed based on submodular function maximization.

中文翻译：

---

基于移动边缘云的工业物联网：利用分层SDN控制器提高边缘智能


工业物联网（IIoT）集成了工业通信、计算、控制等关键技术，可以对制造资源实现灵活管理和动态调度。为了提高工业物联网的边缘智能，本文提出了一种新颖的工业物联网架构，在移动边缘云（MEC）中具有分层控制结构。大规模远程无线电头端（RRH）被划分为多个集群，每个集群配备一台或多台服务器，用于创建虚拟机（VM）来执行IIoT设备的处理任务。所提出的 IIoT 架构基于软件定义网络 (SDN) 将控制平面与数据平面分离。分层控制器提高了控制平面的灵活性和智能性，而RRH和服务器位于同一集群，形成基于MEC的无线接入网络（RAN）并支持RAN功能拆分，提高了数据平面的可扩展性和协作增益。 MEC 中采用深度学习技术，进一步增强边缘智能。此外，我们设计了两种控制方案，一种是集中式的，另一种是分布式的，这在性能和开销之间提供了权衡。最后，以最小化系统延迟为目标，我们以任务调度、VM分配、RRH分配和RAN功能分割的联合优化问题为例。为了寻找解决方案，提出了一种基于子模函数最大化的启发式算法。