A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> SCADA and DCS systems are in the heart of the modern industrial infrastructure. The rapid changes in the networked embedded systems and the way industrial applications are designed and implemented, call for a shift in the architectural paradigm. Next generation SCADA and DCS systems will be able to foster cross-layer collaboration with the shop-floor devices as well as in-network and enterprise applications. Ecosystems driven by (web) service based interactions will enable stronger coupling of real-world and the business side, leading to a new generation of monitoring and control applications and services witnessed as the integration of large-scale systems of systems that are constantly evolving to address new user needs. <s> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> Optimizing the final grinding production indices (GPIs), which include the product particle size and the grinding production rate, to meet the overall manufacturing performance requirements is the main function of automatic control of a grinding circuit (GC). However, the complex and time-varying nature of the GC process dictates that these GPIs cannot be optimized solely by the lower-level distributed control systems (DCS), therefore an operator is often incorporated to manually determine the set-points for the DCS using his/her operational experience. With a human being involved, the performance and even the safety and stability of the GC operation is subject to human errors. Focusing on this practical challenge, this paper proposes an intelligence-based supervisory control strategy that consists of a control loop set-point optimization module, an artificial neural network-based soft-sensor module, a fuzzy logic-based dynamic adjustor, and an expert-based overload diagnosis and adjustment module to perform the control tasks for the GC system. This hybrid system can automatically adjust the set-points for the DCS-controlled grinding system in response to the changes in boundary conditions or the imminent overload conditions, thereby eliminating the need for an operator. Practical applications have shown the validity and effectiveness of the proposed approach. <s> BIB002 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology. <s> BIB003 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> In the smelting process of fused magnesium furnaces (FMFs), frequent changes in the raw material granule size and impurity constituent will cause the arc resistance between the lower end of the electrode and the surface of the molten pool to vary, and thus, the smelting currents fluctuate. Consequently, abnormal conditions, which can arise if the setpoints of electrode currents are not properly adjusted on time, will causethe performance to deteriorate or even the overall operation to stall. Through analysis of the characteristics of different operating conditions, this paper presents a data-driven abnormal condition identification and self-healing control system. The proposed system extracts the identification rules according to the current tracking error, as well as the rate and duration of the current fluctuations, and identifies the abnormal conditions based on rule-based reasoning. The self-healing control is developed using case-based reasoning to correct the current setpoints based on the identification results. The outputs of the control loop track the corrected setpoints, thereby forcing the process to recover from the abnormal conditions. The proposed method and the developed control system have been applied to a real FMF, and substantial improvement is achieved with many benefits provided to the factory. The implementation results show that occurrence of abnormal conditions has been reduced by more than 50%, and the product quality has been increased by more than 2%. <s> BIB004 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> Today, embedded, mobile, and cyberphysical systems are ubiquitous and used in many applications, from industrial control systems, modern vehicles, to critical infrastructure. Current trends and initiatives, such as "Industrie 4.0" and Internet of Things (IoT), promise innovative business models and novel user experiences through strong connectivity and effective use of next generation of embedded devices. These systems generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. Cyberattacks on IoT systems are very critical since they may cause physical damage and even threaten human lives. The complexity of these systems and the potential impact of cyberattacks bring upon new threats. This paper gives an introduction to Industrial IoT systems, the related security and privacy challenges, and an outlook on possible solutions towards a holistic security framework for Industrial IoT systems. <s> BIB005 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> Abstract With the rapid advancement of Information and Communication Technologies (ICT) and the integration of advanced analytics into manufacturing, products and services, many industries are facing new opportunities and at the same time challenges of maintaining their competency and market needs. Such integration, which is called Cyber-physical Systems (CPS), is transforming the industry into the next level. CPS facilitates the systematic transformation of massive data into information, which makes the invisible patterns of degradations and inefficiencies visible and yields to optimal decision-making. This paper focuses on existing trends in the development of industrial big data analytics and CPS. Then it briefly discusses a systematic architecture for applying CPS in manufacturing called 5C. The 5C architecture includes necessary steps to fully integrate cyber-physical systems in the manufacturing industry. Finally, a case study for designing smart machines through the 5C CPS architecture is presented. <s> BIB006 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these ::: recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts. <s> BIB007 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> The migration towards digital control systems creates new security threats that can endanger the safety of industrial infrastructures. Addressing the convergence of safety and security concerns in this context, we provide a comprehensive survey of existing approaches to industrial facility design and risk assessment that consider both safety and security. We also provide a comparative analysis of the different approaches identified in the literature. <s> BIB008 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> This paper proposes a transformative architecture for the normal operation and self-healing of networked microgrids (MGs). MGs can support and interchange electricity with each other in the proposed infrastructure. The networked MGs are connected by a physical common bus and a designed two-layer cyber communication network. The lower layer is within each MG where the energy management system (EMS) schedules the MG operation; the upper layer links a number of EMSs for global optimization and communication. In the normal operation mode, the objective is to schedule dispatchable distributed generators (DGs), energy storage systems (ESs), and controllable loads to minimize the operation costs and maximize the supply adequacy of each MG. When a generation deficiency or fault happens in an MG, the model switches to the self-healing mode and the local generation capacities of other MGs can be used to support the on-emergency portion of the system. A consensus algorithm is used to distribute portions of the desired power support to each individual MG in a decentralized way. The allocated portion corresponds to each MG’s local power exchange target, which is used by its EMS to perform the optimal schedule. The resultant aggregated power output of networked MGs will be used to provide the requested power support. Test cases demonstrate the effectiveness of the proposed methodology. <s> BIB009 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> We consider the problem of resilient control of networked control system (NCS) under denial-of-service (DoS) attack via a unified game approach. The DoS attacks lead to extra constraints in the NCS, where the packets may be jammed by a malicious adversary. Considering the attack-induced packet dropout, optimal control strategies with multitasking and central-tasking structures are developed using game theory in the delta domain, respectively. Based on the optimal control structures, we propose optimality criteria and algorithms for both cyber defenders and DoS attackers. Both simulation and experimental results are provided to illustrate the effectiveness of the proposed design procedure. <s> BIB010 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> Based on the fifth generation mobile communication (5G), and 5G open grid computing environment (5G OGCE), an architecture of heterogeneous large-scale parallel supervisory control and data acquisition/distributed control systems (SCADA/DCS) system of systems (SoS) is presented. Its purpose is to supervise and control and acquire data of process function value of operating electricity, water, air and gas, in natural disasters and accidents caused by incorrectly using them. This architecture, compared with NASA's architecture decomposition of space exploration systems, and with Smart Grid Architecture Model (SGAM), is more suitable for mining and ocean engineering, especially sea-wave power plants, and operating and controlling of processing water, electricity, gas, and air systems in parallel. The architecture can act as a framework for factory automation in 5G OGCE. By using heterogeneous concurrent agents (HCA) in concurrent enterprise (CE), this architecture can realize cyber manufacturing system supported by cloud computing. <s> BIB011 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> With the emergence of the Industrial Internet of Things (IIoT), process industries have started to adopt wireless sensor-actuator networks (WSANs) for control applications. It is crucial to achieve real-time communication in this emerging class of networks, and routing has significant impacts on end-to-end communication delays in WSANs. However, despite considerable research on real-time transmission scheduling and delay analysis for such networks, real-time routing remains an open question for WSANs. This paper presents a conflict-aware real-time routing approach for WSANs, leveraging a key observation that conflicts among transmissions involving a common field device can contribute significantly to communication delays in industrial WSANs, such as WirelessHART networks. By incorporating conflict delays into the routing decisions, conflict-aware real-time routing algorithms allow a WSAN to accommodate more real- time flows while meeting their deadlines. Both evaluations based on simulations and experiments on a physical WSAN testbed show that conflict-aware real-time routing can lead to as much as a three-fold improvement in the real-time capacity of WSANs. <s> BIB012 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> A. CONTROL, NETWORKING AND COMPUTING SYSTEMS IN I-IOT 1) CONTROL <s> In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature. <s> BIB013
Recall that industrial control systems can be categorized into centralized, distributed, and hierarchical control systems. In I-IoT, a critical mission is to realize self-awareness, selfdiagnosis and self-healing in control systems BIB011 , BIB009 , BIB003 , BIB004 . With the enabling of self-awareness, the control system is cognizant of the health condition of manufacturing system and its production quality. With the capacity for selfdiagnosis and self-healing, failures and faults can be detected and classified, and corresponding healing schemes can be implemented. Moreover, the requirements for resilience in industrial control systems, which face to multidimensional disturbances, raise additional challenges BIB012 , BIB005 . In I-IoT systems, the computing platforms collect data from industrial devices and perform data analysis. These control systems operate the industrial systems, including the sensors, actuators and controllers. As can be seen in Figure 11a , control objectives are the enabling of awareness, diagnosis, healing and prediction for industrial control systems. In addition, as shown in Figure 11b , the performance objectives are the realization of efficient management, effective utilization and timely maintenance. As barriers to enabling self-awareness, self-diagnosis and self-healing in industrial control systems, significant overhead exists for data collection, processing, analysis and transmission. For example, continuous monitoring on the condition and health status of large-scale industrial systems will generate a significant volume data BIB006 , BIB007 . The data analysis requires high-capacity computing resources (e.g., cloud computing). Moreover, the timely analysis and processing of the massive data is also important to the timesensitive industrial systems. Extracting useful information from big industrial data, such as hidden patterns and correlations, can assist control VOLUME 6, 2018 systems in making better decisions. One way to effectively process and analyze big data is to leverage predictive learning algorithms, such as supervised, unsupervised, semisupervised and reinforcement learning, which are able to find and explore the hidden structure and correlations of the industrial data BIB013 . For example, Zhou et al. BIB002 designed an intelligence-based control strategy in a DCS-based grinding system. In their proposed system, set-point optimization module, soft-sensor module, and overload diagnosis and adjustment module are included. The system adjusts the set-points in the cases of boundary changes and overload conditions dynamically. Towards the performance objectives of control systems, efficient management incurs close coordination in every step of the manufacturing lifecycle (i.e., prepare, produce, transport, utilize and recycle). For example, the information exchange between manufacturing lifecycle steps can increase its efficiency by balancing demand and supply. In addition, effective utilization means the full use of the production resources, maximizing productivity and minimizing time and resources wasted. Also, the timely maintenance ensures the health of critical infrastructure such that potential damage or risks can be avoided or minimized. Visualization, virtualization and interoperability are key factors to enabling efficient management, effective utilization and timely maintenance BIB001 . This visualization provides better presentation and understanding of the infrastructure and assists operators in making optimal decisions. Notice that the immense quantity of devices and equipment with different configurations will increase the complexity of control in terms of observability and controllability. To satisfy the requirement of resilience on industrial control systems, we shall first understand the resilience of the I-IoT system. However, resilience is a multidimensional property that requires understanding of reactions in the control system to malfunctions, physical component failure, cyberattacks, and so on. The resilience in industrial control systems indicates the ability to survive disturbances  - BIB008 . Moreover, the resilience can be measured by the time of reaction for maintaining system functionality following the disturbances. For instance, Arghandeh et al.  presented a study to define the cyber-physical resilience for the power system. They proposed a framework to assess the resilience of the power system, including system identification, vulnerability analysis, and resilience operations. With the understanding of various disturbances, corresponding mechanisms can be developed to prevent and react to disturbances. Likewise, to improve the resilience of control systems, Yuan et al. BIB010 leveraged game theory mechanisms to design a control method resilient to cyber-attacks (e.g., denial-of-service (DoS) attacks).