A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> D. INTEGRATION OF CLOUD AND EDGE COMPUTING <s> Recently, the industrial Internet of Things (IIoT) has drawn high attention in academia and industry in the context of industry 4.0. In the IIoT, smart IoT devices are adopted to improve production efficiency. But, these devices will generate huge amounts of production data, which need to be processed effectively. To support IIoT services efficiently, cloud computing is usually considered as one of the possible solutions. However, the IIoT services still suffer from the high-latency and unreliable links problem between cloud and IIoT terminals. To combat these issues, fog computing is a promising solution which extends computing and storage to the network edge. In this paper, we are motivated to integrate the fog computing to the cloud-based IIoT to build a cloud-fog integrated IIoT (CF-IIoT) network. To achieve the ultra-low service response latency, we introduce the distributed computing to the CF-IIoT network and propose leveraging the real-coded genetic algorithm for constrained optimization problem(RCGA-CO) algorithm to optimize the load balancing problem of the distributed cloud-fog network. Most importantly, considering the unreliable situation in the CF-IIoT (e.g., fog nodes damage, wireless links outage), we propose a task reallocation and retransmission mechanism to reduce the average service latency of the CF-IIoT network architecture. The performance evaluation results validate that the RCGA-CO-based CF-IIoT and our proposed mechanism can provide ultra-low latency service in IIoT scenario. <s> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> D. INTEGRATION OF CLOUD AND EDGE COMPUTING <s> With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT. <s> BIB002 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> D. INTEGRATION OF CLOUD AND EDGE COMPUTING <s> The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment. <s> BIB003
Regarding integration of cloud and edge computing, several research efforts have made strides, demonstrating improvements with respect to latency, reliability, and resource efficiency in I-IoT systems BIB001 - BIB002 . For example, Shi et al. BIB001 proposed a joint edge-cloud computing architecture to improve latency performance in I-IoT. In particular, they investigated a load balancing strategy among various edge nodes and cloud servers, with the optimal strategy being derived through a proposed genetic algorithm. The evaluation results demonstrated that, with the increase in taskintensity, the joint edge-cloud computing architecture could outperform disjointed edge computing and cloud computing architectures with respect to delay performance. In addition, they leveraged reallocation and retransmission mechanisms to improve the reliability of the production system in cases of node failures. This work can be mapped into <Transmission, PA, QoS> in Figure 10 . In addition, Kuar et al. BIB003 investigated an edge-cloud integration to support big data streaming services in I-IoT. The interplay of cloud and edge has the benefits of energy efficiency, latency and rich computing resources. Besides, the integration of SDN, as middleware, introduces an architecture of three decoupled planes (i.e., data plane, control plane, and application plane). Further investigating flow scheduling in the control plane, they formalized an energy efficiency-bandwidth tradeoff problem and an energy efficiency-latency tradeoff problem to be solved via multiobjective optimization. Then, they proposed a dynamic flow scheduling and routing scheme to compute the optimal routing solutions, which could adapt to the categories of real-time workflows (e.g., batch processing and stream processing). In doing so, the energy efficiency of the integrated edgecloud computing system could be improved. This work can be mapped into cube <Transmission, PA/FA, QoS> in Figure 10 . 