Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.

The emergence of fog computing has improved the performance and efficiency of intelligent manufacturing. In a fog computing platform, fog nodes can provide distributed and proximity resources for tasks related to intelligent manufacturing. Due to the heterogeneous characteristics of fog nodes and the space-time characteristics of intelligent manufacturing, the effective deployment of fog nodes is a precondition for fog computing implementation, it is also a key to providing high-performance services with the fog computing platform. However, traditional deployment strategies cannot satisfy the performance requirements of intelligent manufacturing systems. In this paper, the problem of fog nodes deployment is studied, a fog nodes deployment strategy based on the space-time characteristics (TSBP) is proposed. A fog nodes deployment system model is set up in this paper, the objective function of fog nodes deployment is built up, which the optimization goal is to minimize the computing response time and realize the load balancing of the fog nodes. In addition, the discrete differential evolution algorithm is applied to search the optimal fog nodes deployment solution. Finally, the effectiveness of the proposed TSBP strategy is verified using a candy packaging intelligent production line prototype platform.

In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system. A method based on Computing Mode Selection (CMS) and execution sequences based on the task priority (ASTP) is proposed in this paper. First, a CMS module is designed in the SDN controller and then, after operating the CMS algorithm, each task obtains an optimal computing mode. Second, the task priorities can be calculated according to their real-time performance and calculated amount. According to the task priority, the SDN controller sends a flow table to the SDN switch to complete the task transmission. In other words, the higher the task priority is, the earlier the fog computing service is obtained. Finally, a series of experiments and simulations are performed to evaluate the performance of the proposed method. The results show that our method can achieve real-time performance and high reliability in IIoT.

The reliability of integrated circuit (IC) is their ability to perform specified functions under prescribed conditions and within a specified period. With the rapid development of IC technology, reliability testing has become a key link in ensuring product performance and lifespan. This article reviews the basic concepts, testing processes, key technologies, and challenges and future development of IC reliability testing. First, the significance, classification, and basic processes of reliability testing are introduced. Then, key links such as design verification testing, wafer testing, chip testing, and packaging testing are discussed in detail. Next, key technologies such as Early Failure Rate Testing (EFR), High/Low Temperature Operating Life Tests (HTOL/LTOL), Accelerated Temperature-Humidity and Bias Testing (THB), Highly Accelerated Stress Test (HAST), and Temperature Cycle Testing (TCT) are analyzed. Additionally, challenges such as balancing testing costs and efficiency, innovating testing technologies, and analyzing and processing testing data are explored, along with corresponding strategies. Finally, the development of new testing technologies, the trend towards intelligent testing, and the internationalization of testing standards are discussed as future directions. This article aims to provide a reference for researchers and engineers in the field of IC design and testing to promote the progress and innovation of reliability testing technology.