Multi-tier computation offloading is crucial to address capacity constraints and improve flexibility for mobile devices. However, existing research on multi-layer computing offloading faces challenges like inefficient resource utilization and poor scalability, particularly in handling diverse computational tasks. To address these challenges, this paper proposes a distributed resource allocation and mixed task offloading framework for end-edge-cloud collaborative systems that support partial and full task offloading modes. First, we propose a three-tier network computing architecture and formulate a task-offloading utility maximization problem by jointly optimizing mixed task-offloading and resource allocation. The proposed problem is a mixed integer nonlinear program (MINLP), which we solve by decomposing it into two subproblemsresource allocationandtask offloading. Edge computing resources and bandwidth allocation can be independently optimized at each edge node with a fixed task offloading strategy. Cloud computing resource allocation, while convex, involves a global constraint, which we solve in a decentralized manner using a multi-agent optimization approach. Then, we propose a joint task offloading and resource allocation optimization algorithm, CNO-TORA, to obtain the solution to the formulated problem. The algorithm is supported by strong theoretical guarantees and is almost surely convergent to a globally optimal solution. Experimental results on a real dataset demonstrate that our algorithm is scalable to large-scale networks and outperforms baselines, achieving improvements in average system utility ranging from 4.01%-28.15%.

Deep learning methods have achieved state-of-the-art performance in industrial fault diagnosis within the supervised learning paradigm. However, annotated data are scarce in industry, which can lead to overfitting and hinder their application. To address this issue, this article proposes a semisupervised learning framework that leverages self-supervised learning on abundant unlabeled data and supervised learning on limited labeled data simultaneously. Self-supervised learning captures inherent evolutionary dynamics, while supervised learning focuses on discriminative features. Specifically, a cross-prediction task on two augmented views of unlabeled data is devised using contextual representation. These contextual representations are used to construct a relational graph of unlabeled samples, which is then aligned with the corresponding logits graph. By facilitating interactions between the two tasks, the proposed framework achieves efficient fault diagnosis. Experiments on the Tennessee Eastman process and three-phase flow Facility datasets demonstrate the superiority of the proposed framework over other label-efficient methods.

Infinite delays, especially mixed infinite delays (MIDs), always pose a great challenge for exponentially stability analysis of neural networks (NNs). In this article, we construct a new Lyapunov functional that contains an auxiliary function with the ability to compress infinite time delays to bounded ones, which can remove some of the previous assumptions on NNs systems. Then, several new sufficient conditions to guarantee the exponential stabilization of state-dependent switched inertial NNs with MIDs are derived under the designed adaptive intermittent controller. Finally, numerical simulations are provided to illustrate the validity of the obtained results.