With the rapid development of industrial Internet of Things (IIoT), the edge-cloud collaboration architecture combining the powerful computing ability of cloud computing with the low latency of edge computing plays an increasingly important role in providing the computing resources and reducing the latency for IIoT. However, under this architecture, existing methods in scheduling the resources for IIoT often focus on latency and energy consumption but ignore some other important factors especially the reliable factor, thereby making it difficult for them to adapt to real-world IIoT scenarios. To this end, we propose a reliable resource scheduling method with knowledge transfer for edge-cloud collaboration-enabled IIoT. Specifically, we first model the resource scheduling as a many-objective optimization problem, considering these optimized objectives: latency, energy consumption, load balance, resource utilization, and trust measure between tasks and servers. Then, we develop a knowledge transfer accelerated clustering evolutionary algorithm (KTCEA) for many-objective optimization to solve the model, where the knowledge transfer aims at accelerating the evolution and the clustering makes the population converge from various directions. Under the collaboration of knowledge transfer and clustering, KTCEA can utilize the small population size to effectively search the objective space, and thus have the high real-time performance. Extensive experiment results on a benchmark test suite and the constructed model demonstrate that KTCEA is highly competitive compared with some advanced methods and our method can efficiently achieve the resource scheduling for edge-cloud collaboration-enabled IIoT, respectively.

Integrating digital surface models (DSM) with remote sensing image has emerged as a pivotal strategy for remote sensing semantic segmentation. While prevailing dual-branch convolutional frameworks independently process DSM and remote sensing image, their inherent limitations in modeling long-range contextual dependencies persist due to convolutional operations’ local receptive fields. Notably, the hierarchical feature in U-model inherently embodies multiscale complementary relationships, yet current multimodal fusion paradigms insufficiently exploit this architectural advantage. In this letter, we propose an efficient three-branch structure encoder to simultaneously extract DSM features and local and global features of remote sensing images. Notably, we leverage the recently introduced Mamba instead of Transformer to capture long-range dependencies, significantly reducing computational complexity while maintaining competitive performance. The extracted features are integrated using the tri-feature complementary fusion (TriFusion) module, which employs a stepwise fusion strategy to learn spatial complementarity between global and local features and channel-wise complementarity between remote sensing images and DSM. Additionally, we introduce a cross-layer feature guidance (CLFG) module within the skip connections to improve segmentation accuracy by facilitating enhanced cross-layer feature propagation. Extensive experiments conducted on two high-resolution remote sensing datasets, ISPRS Vaihingen and Potsdam, demonstrate that the proposed MambaTriNet surpasses existing state-of-the-art methods in performance metrics, achieving 83.84% and 86.05% mIoU, respectively.

With the rapid development of 5G, numerous data is generated in the blockchain-enabled industrial Internet of Things (IIoT). Although these peers in the blockchain system have the storage ability, they are far from meeting the storage requirements of the generated data. In addition, all data is stored in the blockchain network, which is unfriendly to applications that require real-time information. To address the above storage problems, this article proposes a block storage optimization method for blockchain-enabled IIoT, whose core idea is to conditionally select some blocks to store in the cloud. This method firstly models the selection conditions of blocks as a many-objective optimization problem, where the selection conditions include using probability, storage cost, space occupation, and transmission cost. Then, a cascading selection-based evolutionary algorithm (CSEA) for many-objective optimization is developed to solve the model and thereby obtain the optimal blocks stored in the cloud, where CSEA adopts the diversity-first principle. Finally, CSEA is first compared with seven state-of-the-art methods on two benchmark test suites for validating its ability to obtain reliable experimental results, and then is used to solve the proposed model. The corresponding results demonstrate that CSEA has high competitiveness, and our method can effectively address the storage problems above. In summary, this article provides a novel method for addressing the storage problem in the blockchain-enabled IIoT.

The incipient fault detection of a complex industrial process is a challenging problem for traditional dynamic detection methods. Traditional dynamic detection methods usually decouple the correlations among the variables and dynamic correlations simultaneously, which makes the two types of correlations mixed and may lead to performance deterioration in long-sequence dynamic detection. Some incipient faults may not change the amplitudes of process variables but change the long-sequence dynamic features. Based on the$T^{2}$statistic and matrix multiplication transformation ($T^{2}$S-MMT), traditional dynamic detection methods can detect many faults effectively. However, the$T^{2}$S-MMT can not effectively detect some incipient faults due to the above two types of correlations mixed. In order to overcome the shortcomings of$T^{2}$S-MMT and improve the detection ability of some incipient faults, this paper proposes a fault detection method based on the dynamic k-nearest neighbor model and Dual Control Chart (DKNN-DCC), which can improve the incipient fault detection performance by using long-sequence dynamic detection. The proposed method is verified by the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process. The experimental results show the effectiveness of the proposed method in incipient fault detection compared with traditional dynamic detection methods.Note to Practitioners—This paper presents a novel incipient fault detection method, which directly mines the long-sequence dynamic abnormal information from the process variable and overcomes the problem of some abnormal information being submerged in the$T^{2}$statistic calculated based on the matrix multiplication transformation. The proposed method can detect incipient faults that are not easily detected by some traditional methods and can help operators find the abnormal and avoid more serious losses. The structure of the proposed method jumps out of the frameworks of traditional dynamic detection methods, which is feasible to apply to different stable industrial processes.

Traditional projection dynamic monitoring methods focus on simultaneously decoupling the correlations among the process variables and the autocorrelations of the variables, which leads to a mixing problem of the two correlations. The mixing problem decreases the ability to model the dynamic correlations, thus decreasing the detection rates (DRs) of some faults. Considering that the projection matrix may cause the mixing of the two correlations, this article proposes a dynamic monitoring method based on directly monitoring original variables. This article first adopts the kernel least-mean-squares (KLMS) method to establish a univariate dynamic model, then adopts the univariate dynamic model and the dual control chart (DCC) to build the multivariate direct monitoring method, which is named the KL2C method (KL represents KLMS and 2 C represents DCC). Then, the dynamic nearest neighbor kernel space (DNNKS) is proposed to overcome the redundant dimensions problem of the KL2C method, which is named the DKL2C method (D represents DNNKS). Furthermore, based on the joint control chart, this article puts forward a dynamic monitoring method of two sequences, which both considers the advantages of the projection dynamic monitoring method and the direct dynamic monitoring method together. Finally, this article utilizes the Tennessee Eastman process and the continuously stirred tank reactor process to verify the effectiveness of the proposed methods.

This paper proposes a general multi-objective particle swarm optimizer based on R2-indicator and decomposition (called R2-MOPSO) to deal with multi-objective optimization problems and then to solve many-objective optimization problems. R2-MOPSO makes use of the R2 contribution of the archived solutions to select global best leaders and update the swarm. R2-MOPSO uses decomposition method for selecting the personal best leaders and updates them for each particle in the population. In order to enhance the diversity of the particles, elitist-learning strategy and gaussian learning strategy are used. Our proposed algorithm is evaluated adopting benchmark test problems and indicators reported in the specialized literature, comparing is results with respect to those obtained by the state-of-the-art multi-objective evolutionary algorithms. Our preliminary results indicate that our proposal is competitive with respect to state-of-the-art multi-objective evolutionary algorithms, being particularly suitable for solving multi-objective and many-objective optimization problems.