Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

To confront the great challenge of industrial big data, the software-defined industrial networks (SDINs) are introduced to dynamically coordinate these data flows among the heterogeneous and distributed computing resources. Deciding how to more efficiently schedule many homogeneous computing tasks, which extensively appear in SDIN, becomes of critical importance. To this end, this article first illustrates some related notations and assumptions of the homogeneous computing tasks and computing networks, from which a new targeted optimization model is formulated. Then, the model is significantly enhanced by reformulating all the nonlinear constraints and inventively establishing the symmetry-breaking constraints and computation time cuts. Furthermore, considering the computational complexity, the scheduling process with many homogeneous computing tasks is further viewed as two associated phases: computing nodes assignment and tasks sequencing. As a result, a novel bilevel decomposition algorithm is proposed using Lagrangian decomposition and a new form of Lagrangian relaxation. Finally, a real industrial scenario is chosen, and three comparison algorithms are used to demonstrate the preponderant performance of the proposed algorithm.

Industrial Internet of Things (IIoT) is changing the way in which factories operatewith the help of various industrial applications. However, the execution uncertainty of computing tasks has always been ignored in IIoT applications. In this paper, we define a novel conditional task graph to describe the execution uncertainty and present a generation algorithm to obtain all task scenario graphs and corresponding occurrence probabilities. Then, a new IIoT-oriented multi-task scheduling model under executionuncertainty is built. This model is simplified by reformulating the nonlinear constraints and subsequently decomposed into several small-scale models using Lagrange multipliers, from which a decomposition-based algorithm is derived to solve the decomposedsmall-scalemodels and progressively acquire a well-optimized solution of the initial model. Furthermore, a patching algorithm is constructed to improve the obtained solution. Finally, many test cases are generated, and fourselected algorithms are taken for comparison to evaluate the performance of our algorithms. The results demonstrate that our algorithms remarkably outperform the others. Besides, the solutions of the proposed algorithms can completely satisfy the execution deadline constraints of differenttask scenarios.

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated heterogeneous computing tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.

With the increasing awareness of environmental protection, sustainable manufacturing has become an important component in various industries. As an essential foundation for sustainable strategy, safe and reliable operation and maintenance of nuclear power resources is crucial, requesting agile and precise response and diagnosis of equipment failure signals. Due to security requirements, nuclear power plants strictly isolate operating data and form an actual data island. Simultaneously, the insufficient amount of fault sample data makes it difficult to establish an accurate fault diagnosis model. How to establish a stable and reliable nuclear power steam turbine vibration fault diagnosis model across different nuclear power plants and nuclear equipment has become a big problem. To achieve secure model aggregation without violating client privacy, federated learning (FL) has become a research hot spot for model aggregation, but it ignores the differences between source clients and fails to capture domain‐invariant features during local training, which hinders its further development. To address this challenge, a federated deep domain adaptation‐based framework considering privacy‐preserving (FL‐DDA) is proposed for operations and maintenance in nuclear power plants. The framework performs feature extraction locally in source nuclear power plants and targets nuclear power plants, such that the features are shared securely without revealing data privacy. At the same time, domain adversarial training is integrated into the local model training to realise the transfer of vibration fault diagnosis knowledge. Furthermore, an adaptive weight mechanism is devised to facilitate the adaptive adjustment of model weights in federated aggregation. Finally, a desensitised vibration dataset in nuclear power steam turbines is applied for validation, and FL‐DDA is compared with other existing methods. Under the premise of data privacy security, the proposed FL‐DDA framework proves to outperform its peers in vibration fault diagnosis and domain adaptation.