Fog computing is emerging as a promising mode to meet the stringent requirement of low latency in industrial Internet of Things (IIoT). By dynamically offloading part of the computation-intensive tasks from a fog node to a cloud server, the computation experience of users can be further improved in fog computing systems. In this paper, we develop an energy-optimal dynamic computation offloading scheme (EDCO) for IIoT in a fog computing scenario. The purpose is to minimize energy consumption when computation tasks are accomplished within a desired energy overhead and delay. Specifically, we first formulate an energy minimization computation offloading problem with delay, energy and other network resource constraints. To address this optimization problem, an accelerated gradient algorithm with joint optimization of the offloading ratio and transmission time is proposed; it can find the optimal value with a fast speed that improves the convergence speed of traditional methods. Subsequently, to better meet the stringent energy and latency requirements of IIoT applications, the dynamic voltage scaling (DVS) technique is integrated into the above solution, and we develop an alternating minimization algorithm to achieve energy-optimal fog computation offloading by jointly optimizing the offloading ratio, transmission power, local CPU computation speed and transmission time. Finally, the numerical results reveal that the proposed offloading scheme is superior to the local computing, full offloading and partial offloading with fixed computation speed schemes in terms of energy consumption and completion time. We also confirm the convergence rate advantage of the accelerated algorithm.

In this paper, first, the designed layer-aware fog computing architecture provides effective support for efficient and secure data collection and computation offloading. Then the proposed sampling perturbation encryption method protects data privacy against eavesdroppers and active attackers without sacrificing data correlation. Furthermore, the developed data processing method at fog nodes reduces the amount of redundant data transmissions significantly, and the formulated optimization model for the measurement matrix ensures the high precision of data reconstruction. Particularly, a completion time minimization problem is formulated for fog computation offloading, and an efficient offloading decision algorithm is developed to find the minimum completion time by determining the optimal offloading proportion with joint optimal allocation of local CPU, external CPU and channel bandwidth resources. Finally, the illustrative results reveal that the proposed scheme is an efficient data collection and computation offloading scheme with a strong privacy preservation property. For example, when the temporal compression ratio is 0.5, the redundant data can be reduced by 65% at fog node with a low recovery error 0.0139. At the same time when the task size is 9 Mb, the completion time of computation task at fog node can be reduced by 14.6% compared with other computation offloading method.

With the rapid development of mobile industrial applications and due to the limited coverage of static edge servers, traditional edge computing technology has great limitations in dynamic environmental applications. This paper proposes a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading mechanism for satisfying the dynamic service requirements in industrial networks. Specifically, a three-layer network model of digital twins and a decentralized network of task resources are first constructed to handle the mobility of user terminals and the relevance of tasks. Then, based on the comprehensive consideration of mobility, associated tasks, computing resources and offloading decisions, an optimization problem is formulated to minimize the weighted sum of the execution delay and energy consumption of all tasks for all users. Additionally, a deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) algorithm is proposed to solve this optimization problem. Based on the differences in each edge cloud, this algorithm sets the priority of the shared experience pool and selects the most effective experience samples to complete better learning and training. It also utilizes a distributed learning method to learn the probability of an approximate reward distribution and optimizes network parameters through cloud-edge collaboration to achieve faster optimal offloading decision. Finally, a large number of simulation results show that the proposed algorithm has the characteristics of fast convergence and high stability, and it can obtain the optimal offloading decision with the lowest total cost.

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of patients, which may limit the diagnosis accuracy fundamentally. In this paper, we propose a metadata and image features co-aware personalized federated learning scheme for smart healthcare. Specifically, we construct an intelligent diagnosis model, by which users can obtain fast and accurate diagnosis services. Meanwhile, a personalized federated learning scheme is designed to utilize the knowledge learned from other edge nodes with larger contributions and customize high-quality personalized classification models for each edge node. Subsequently, a Naïve Bayes classifier is devised for classifying patient metadata. And then the image and metadata diagnosis results are jointly aggregated by different weights to improve the accuracy of intelligent diagnosis. Finally, the simulation results illustrate that, compared with the existing methods, our proposed algorithm achieves better classification accuracy, reaching about 97.16% on PAD-UFES-20 dataset.

Traditional solutions for human action recognition usually rely on sensor or video methods. However, these methods have some limitations, such as inconvenient portability, light intensity influence, privacy protection, etc. In this article, an RFID-based nonwearable human action recognition scheme is proposed. In order to reduce the occlusion effect of the human body on the signal and increase the diversity of the reflected signal, a tags array is constructed. The data of phase and RSSI are fused as feature data to enhance the diversity of data. Furthermore, a combined processing method is proposed to eliminate thermal noise generated by the equipment and reduce the interference caused by the environment. Then, an action segmentation algorithm is designed to align the RF signals of human action. Finally, an efficient human action signal classification model is constructed using the spatiotemporal graph convolutional neural network (STGCN). Extensive experiments demonstrate that the overall accuracy rate of the system for human action recognition is 92.8%. Compared with the comparative mainstream recognition algorithms, STGCN shows better classification performance in terms of identification precision. In addition, multimodal RFID data fusion also improves the accuracy of identification.