Mobile Edge Cloud-Based Industrial Internet of Things: Improving Edge Intelligence with Hierarchical SDN Controllers

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.