Distributed Multidomain Resource Allocation for IIoT-Based Control Systems

Industrial Internet of Things (IIoT)-based control is growing rapidly, such as smart factories and industrial automation. In practice, imperfect wireless networks and time delay caused by delayed completion of computing tasks in IIoT may deteriorate the control performance. To enhance the performance of the control system, a multidomain resource allocation problem is formulated by co-designing control, communication, and computation resources, which is a mixed-integer nonlinear programming (MINLP) problem. In this article, a bilevel optimization framework is proposed to solve the MINLP, in which the sharing decision is derived in the upper level, and then, the optimal allocation of multidomain resources is derived in the lower level. A control-aware distributed bilevel (CADB) algorithm is developed, where these two levels interact with each other. In each round, the upper level optimization problem is updated based on the last resource allocation and solved by a primal-decomposition algorithm with provable finite-time feasibility. Then, according to the newly derived sharing decision, the lower level optimization problem is solved by the proposed mixed proximal-gradient-tracking algorithm. It is shown that CADB algorithm enables control systems to achieve enhanced control performance and energy consumption. Finally, simulations are conducted to verify the effectiveness of the proposed algorithm.