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Industrial Automation (IA) and Artificial Intelligence (AI) need an integrated platform. Due to the uncertainty of the time required for training or reasoning tasks, it is difficult to ensure the real-time performance of AI in the factory. Thus in this paper, we carry out a detailed survey on cloud-edge computing-based Industrial Cyber Intelligent Control Operating System (ICICOS) for industrial automation and artificial intelligence. The ICICOS is built based on IEC61499 programming method and used to replace the obsolete Programmable Logic Controller (PLC). It is widely known that the third industrial revolution produced an important device: PLC. But the finite capability of PLC just only adapts automation which will not be able to support AI, especially deep learning algorithms. Edge computing promotes the expansion of distributed architecture to the Internet of Things (IoT), but little effect has been achieved in the territory of PLC. Therefore, ICICOS focuses on virtualization for IA and AI, so we introduce our ICICOS in this paper, and give the specific details. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is one of the efficient technologies to provide satisfying quality of experience (QoE) for emerging computation-intensive applications in internet of things (IoT). However, some new challenges will be encountered when MEC is applied in a large-scale IoT with massive access devices or heavy traffic loads such as load balancing and traffic offloading. Aiming at the solution of these problems, this paper proposes a learning-based traffic control architecture for IoT with MEC. Moreover, a deep-learning-based load balancing framework is developed to control user association in IoT. The user association is determined at each IoT access points (IAP) by the deep neural network (DNN), which is the duplication of the well-trained DNN with the global network information. In addition, we propose a reinforcement-learning-based partial traffic offloading scheme to reduce the traffic origination. The IoT devices are able to independently decide its offloading radio according to the channel quality information, service requirement, and workload of the IAP. Simulation results indicate that the proposed deep-learning-based load balancing scheme is able to achieve uniform traffic distribution, and meanwhile our partial offloading policy can significantly reduce the network traffic. [ABSTRACT FROM AUTHOR]

In recent years, the worldwide nuclear industry has developed vigorously. At the same time, incidents of radioactive source loss have also occurred frequently. At present, most of the search for radioactive sources uses manual search, which is inefficient and the search personnel are vulnerable to radiation damage. Sending the robot into the area where the uncontrolled radioactive source may be located for search is different. Not only the efficiency can be improved, but the personnel are also prevented from being exposed to radiation. Therefore, designing a radioactive source search robot has great practical significance. This paper mainly introduces the design and implementation of an intelligent search robot for radioactive sources based on edge computing, and intends to provide some ideas and directions for the research of intelligent robots for searching for radioactive sources. This paper proposes a research method for the design and implementation of a radioactive source intelligent search robot based on edge computing, including intelligent edge computing and gamma ray imaging algorithms, which are used to carry out related experiments on the design and implementation of a radioactive source intelligent search robot based on edge computing. The experimental results in this paper show that the average resolution of the radioactive source search robot is 90.55%, and the resolution results are more prominent. [ABSTRACT FROM AUTHOR]

There has been a lot of research on edge-computing task offloading in deep reinforcement learning (DRL). Deep reinforcement learning is one of the important algorithms in the current AI field, but there is still room for improvement in the time cost and adaptive correction ability of the algorithm. This paper studies the application of DRL algorithms in edge-computing task offloading, and its key innovation is to propose an MADRLCO algorithm, which is based on the design idea of the Actor–Critic framework, uses the DNN model to act as an Actor, and can more accurately locate the initial decision through iterative training, and use the LSTM model to optimize the Critic, which can be more accurate. The optimal decision can be located in a short period of time. The main work of this paper is divided into three parts: (1) The AC algorithm of the Actor–Critic framework in DRL is proposed to be applied to edge-computing task offloading. (2) To address the weak generalization ability of the basic version of the Actor–Critic algorithm in multi-objective optimization, the sequential quantitative correction and adaptive correction parameter K method are used to optimize the Critic frame, thereby improving the generalization ability of the model in multi-objective decision-making and improving the rationality of decision-making results. (3) Aiming at the problem of large time cost in the critical framework of the model, a search algorithm for resource allocation-related parameters based on the time-series prediction method is proposed (time-series forecasting is a research branch of pattern recognition), which reduces the time overhead of the algorithm and improves the adaptive correction capability of the model. The algorithm in this paper can adapt to not only the time-varying network channel state, but also the time-varying number of device connections. Finally, it is proved by experiments that compared with the DRL calculation offloading algorithm based on DNN plus binary search, the MADRLCO algorithm reduces the model training time by 66.27%, and in the environment of the time-varying number of devices in the metasystem, the average model average. The standard calculation rate is 0.0403 higher than that of the current optimal algorithm. [ABSTRACT FROM AUTHOR]