A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Abstract This paper presents the design of a smart home system based on Internet of Things (IOT) and service component technologies. The current situation of IOT has been analyzed in detail. An approach based on SOA and component technology has been proposed and applied, which can help to realize every-changing dynamic semantic integration of the web services. Furthermore, the software architecture and main modules are explained as well. Finally, this paper discussed the heterogeneous information fusion in the Internet of Things. <s> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically. <s> BIB002 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Connectivity is probably the most basic building block of the IoT paradigm. Up to now, the two main approaches to provide data access to things have been based on either multihop mesh networks using short-range communication technologies in the unlicensed spectrum, or long-range legacy cellular technologies, mainly 2G/GSM/GPRS, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-gigahertz frequency bands, used to realize access networks with star topology referred to as low-power WANs (LPWANs). In this article, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, particularly for typical smart city applications. <s> BIB003 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Abstract Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber-Physical Systems (CPS), within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5-level architecture is proposed as a guideline for implementation of CPS. <s> BIB004 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Fog/edge computing has been proposed to be integrated with Internet of Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between cyber-physical systems and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications. <s> BIB005 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> The smart grid, as one of typical applications supported by Internet of Things, denoted as a re-engineering and a modernization of the traditional power grid, aims to provide reliable, secure, and efficient energy transmission and distribution to consumers. How to effectively integrate distributed (renewable) energy resources and storage devices to satisfy the energy service requirements of users, while minimizing the power generation and transmission cost, remains a highly pressing challenge in the smart grid. To address this challenge and assess the effectiveness of integrating distributed energy resources and storage devices, in this paper, we develop a theoretical framework to model and analyze three types of power grid systems: 1) the power grid with only bulk energy generators; 2) the power grid with distributed energy resources; and 3) the power grid with both distributed energy resources and storage devices. Based on the metrics of the power cumulative cost and the service reliability to users, we formally model and analyze the impact of integrating distributed energy resources and storage devices in the power grid. We also use the concept of network calculus, which has been traditionally used for carrying out traffic engineering in computer networks, to derive the bounds of both power supply and user demand to achieve a high service reliability to users. Through an extensive performance evaluation, our evaluation results show that integrating distributed energy resources conjointly with energy storage devices can reduce generation costs, smooth the curve of bulk power generation over time, reduce bulk power generation and power distribution losses, and provide a sustainable service reliability to users in the power grid. <s> BIB006 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Abstract Originally initiated in Germany, Industry 4.0, the fourth industrial revolution, has attracted much attention in recent literatures. It is closely related with the Internet of Things (IoT), Cyber Physical System (CPS), information and communications technology (ICT), Enterprise Architecture (EA), and Enterprise Integration (EI). Despite of the dynamic nature of the research on Industry 4.0, however, a systematic and extensive review of recent research on it is has been unavailable. Accordingly, this paper conducts a comprehensive review on Industry 4.0 and presents an overview of the content, scope, and findings of Industry 4.0 by examining the existing literatures in all of the databases within the Web of Science. Altogether, 88 papers related to Industry 4.0 are grouped into five research categories and reviewed. In addition, this paper outlines the critical issue of the interoperability of Industry 4.0, and proposes a conceptual framework of interoperability regarding Industry 4.0. Challenges and trends for future research on Industry 4.0 are discussed. <s> BIB007 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> With the introduction of the Internet of Things (IoT) and cyberphysical system (CPS) concepts in industrial application scenarios, industrial automation is undergoing a tremendous change. This is made possible in part by recent advances in technology that allow interconnection on a wider and more fine-grained scale. The purpose of this article is to review technological trends and the impact they may have on industrial communication. We will review the impact of IoT and CPSs on industrial automation from an industry 4.0 perspective, give a survey of the current state of work on Ethernet time-sensitive networking (TSN), and shed light on the role of fifth-generation (5G) telecom networks in automation. Moreover, we will point out the need for harmonization beyond networking. <s> BIB008 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Industrial systems are more and more dominated by software. In addition, this software is present across several compute domains, from decentralized edge to centralized datacenters and clouds. While the productivity of application lifecycle management has tremendously improved in the area of cloud computing, there is no homogeneous and seamless environment to build, deploy and operate the software across these domains. This leads to a separation, inefficient processes, and duplicate efforts to provide software that runs across the different layers. This paper introduces the concept of seamless computing, which provides a seamless computing environment for multi-domain applications, supporting the mobility of workloads between cloud and edge. The approach is based on transferring established, de-facto standard cloud computing technologies to resource-constrained compute environments in the edge. After defining high-level requirements for seamless computing, a functional reference model is derived, and existing cloud technologies are discussed and selected according to their related capabilities. <s> BIB009 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> In industrial Internet of Things (IIoT) applications for smart manufacturing system, efficient allocation of the carrier and computing resources is crucial. However, existing resource assignment schemes in smart manufacturing system cannot provide timely provision of resources to the inherently dynamic and bursty user demands. To reflect real-time supply and demand for smart manufacturing resources, several research results on auction-style resource assignments have been introduced; however, security, privacy, and trust computing related issues are not actively discussed in the results. The resources should be assigned to devices according to the system policy, which depends on the information provided by IIoT devices. If there are any resource demanding devices, they can report manipulated malicious information for their own interest to obtain more resources. That is, the smart manufacturing system may be vulnerable due to selfish smart manufacturing devices’ behaviors. This reduces the efficiency of the entire system and moreover ceases the plant-wide process. While many research contributions related to the trust computing aim at detecting malicious nodes, this paper presents a novel view of trust computing by showing why devices inside the smart manufacturing system have to act honestly. In this paper, a Vickrey–Clarke–Groves auction-based hierarchical trust computing algorithm is proposed for: 1) computing carrier resources required for wireless communication between IIoT devices and gateways and 2) distributing CPU resources for processing data at central processing controller. Last, simulation results demonstrate that the utilities of each participant are maximized when the IIoT devices and gateways are trustful. <s> BIB010 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> The Internet of Things (IoT), which seamlessly interconnects heterogeneous devices with diverse functionalities, is an attractive choice for the large-scale petrochemical industry to develop an integrated system. With the industrial revolution, efforts have mainly been focused on factory automation, transportation security, and surveillance. The IoT plays a major role in factories with its new methods of data management and data collection. However, the IoT is still at an early stage in large-scale petrochemical plants due to the multiple coexisting heterogeneous networks in harsh and complex large-scale industrial networks. This paper presents a comprehensive survey on the IoT in large-scale petrochemical plants as well as recent activities in communication standards for the IoT in industries. This paper addresses the key enabling middleware approaches, e.g., an industrial intelligent sensing ecosystem (IISE), which allows rapid deployment and integration of heterogeneous wireless sensor networks with advancements in crowdsensing based services. In addition, this survey highlights the research issues of data management in the IoT for large-scale petrochemical plants. <s> BIB011 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Mobile edge computing (MEC) yields significant paradigm shift in industrial Internet of things (IIoT), by bringing resource-rich data center near to the lightweight IIoT mobile devices (MDs). In MEC, resource allocation and network economics need to be jointly addressed to maximize system efficiency and incentivize price-driven agents, whereas this joint problem is under the locality constraints, i.e., an edge server can only serve multiple IIoT MDs in the vicinity constrained by its limited computing resource. In this paper, we investigate the joint problem of network economics and resource allocation in MEC where IIoT MDs request offloading with claimed bids and edge servers provide their limited computing service with ask prices. Particularly, we propose two double auction schemes with dynamic pricing in MEC, namely a breakeven-based double auction (BDA) and a more efficient dynamic pricing based double auction (DPDA), to determine the matched pairs between IIoT MDs and edge servers, as well as the pricing mechanisms for high system efficiency, under the locality constraints. Through theoretical analysis, both algorithms are proved to be budget-balanced, individual profit, system efficient, and truthful. Extensive simulations have been conducted to evaluate the performance of the proposed algorithms and the simulation results indicate that the proposed DPDA and BDA can significantly improve the system efficiency of MEC in IIoT. <s> BIB012 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> The large amount of data among billions of devices deployed for Industrial Internet of Things (IIoT) cause a massive energy consumption. Driven by the pursuit of green communication, this paper presents a space-reserved cooperative caching scheme for IIoT in the fifth generation mobile heterogeneous networks, where the cache space in a base station is divided into two parts, one is used to store the prefetched data from the servers ahead of the device request time and the other is reserved to store the temporarily buffering data in the wireless transmission queue at the device request time. With the constraint that the quality of service is guaranteed, we propose an algorithm to obtain the optimal proportion between the two parts of the cache space for the purpose of reducing the average energy consumption. Simulation results verified that the proposed caching scheme is more efficient than the conventional one with respect to the average energy consumption. <s> BIB013 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Industrial Internet of things (IIoT) is an emerging technology with a large number of smart connected devices having sensing, storage, and computing capabilities. IIoT is used in a wide range of applications such as transportation, healthcare, manufacturing, and energy management in smart grids. Most of the solutions reported in the literature for secure communications are not suitable for the aforementioned applications due to the usage of traditional TCP/IP-based network infrastructure. So, to handle this challenge, in this paper, a software-defined network (SDN) enabled multi-attribute secure communication model for an IIoT environment is designed. The proposed scheme works in three phases: 1) an SDN-IIoT communication model is designed using a cuckoo-filter -based fast-forwarding scheme, 2) an attribute-based encryption scheme is presented for secure data communication, and 3) a peer entity authentication scheme using a third party authenticator, Kerberos , is also presented. The proposed scheme has been evaluated using different parameters where the results obtained prove its effectiveness in comparison to the existing solutions. <s> BIB014 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> I. INTRODUCTION <s> Fog computing provides a more flexible service environment than cloud computing. The lightweight fog environment is suitable for industrial applications. In order to strengthen service scalability, container virtualization has been proposed and studied in recent years. It is vital to explore the tradeoff between service scalability and operating expenses. This paper integrates the hypervisor technique with container virtualization, and constructs an integrated virtualization (IV) fog platform for deploying industrial applications based on the virtual network function. This paper presents a fuzzy-based real-time autoscaling (FRAS) mechanism and implements it in the IV fog platform. The FRAS mechanism provides a dynamic, rapid, lightweight, and low-cost solution to the service autoscaling problem. Experimental results showed that the proposed FRAS mechanism yields a better service scale with lower average delay, error rate, and operating expenses compared to other autoscaling schemes. <s> BIB015
In smart manufacturing systems BIB010 , along with other typical cyber-physical systems (CPS) like the smart grid BIB005 , BIB006 , modern information communication technologies (computation, control, communication, etc.) can be leveraged to improve the monitoring and control abilities of physical systems (manufacturing equipment and workflow, electrical grid transmission and generation, etc.). To realize the vision of the next generation industrial revolution (known as Industry 4.0), smart manufacturing is a necessity, as massive numbers of smart sensors and actuators (otherwise known as machine devices) will be interconnected and integrated into industrial systems using the advanced technologies developed for the Internet of Things (IoT) and CPS BIB007 . Moreover, these technologies are becoming increasingly mature, reliable, and widely adopted, with the number of machine devices deployed worldwide projected to grow to 75.4 billion by 2025 according to a recent market research study  . In the context of Industry 4.0, the requirements of response time, network latency, and reliability are especially critical, as the entire workflow of the manufacturing process will be carried out automatically, without human intervention where possible. Thus, the data transmission and decisionmaking technologies shall be optimized to be as timely and accurate as possible. In the recent past, IoT has become a highly active research area, as it enables the interconnection of anything, anytime and anywhere BIB005 ,  , BIB003 . IoT has been applied to interconnect unprecedented quantities of devices for consumer applications (home appliances, transportation, mobile devices, etc.). Those consumer applications (e.g., smart home, smart city, smart grid, and smart transportation) can provide convenience, efficiency and intelligence to consumers to better manage their personal time and resources BIB001 - BIB002 . Extending the technology, Industry 4.0 envisions the adoption of IoT for use in manufacturing, and has great potential to improve the productivity, efficiency, safety and intelligence of industrial factories and plants BIB004 , [16] . Thus, Industrial-IoT (I-IoT) indicates the nature of the application of IoT to be industrial manufacturing, facilitating the interconnection of ''anything'' (sensors, actuators, controllers, production lines, equipment, etc.) in an industrial production and automation context. An I-IoT system, as a typical CPS, is composed of two key components: the cyber systems and the physical systems. The cyber systems include control, networking and computing infrastructures that enable the operation, interconnection, and intelligence of the industrial systems. The physical systems are the manufacturing and automation systems that leverage industrial devices to undertake designated production and automation tasks. There have been some recent research efforts towards investigating I-IoT (understanding the I-IoT system in general, investigating specific issues in I-IoT, leveraging state-of-the-art techniques for I-IoT, etc.). For instance, Shu et al. BIB011 surveyed the challenges and research issues on data management in I-IoT and conducted a case study using a large-scale petrochemical plant. In addition, in investigating a specific I-IoT problem, Sun et al. BIB012 applied auction-based mechanisms to address the computing resource allocation problem, while Jeong et al. BIB010 addressed the trust issues in resource allocation. Moreover, some research efforts have leveraged advanced communication techniques, such as 5G and software-defined networking (SDN) for I-IoT BIB013 , BIB014 . In this paper, we provide a comprehensive survey of IIoT from a CPS perspective, focusing primarily on control, networking, and computing. In I-IoT, control systems play the critical role of operating massive number of industrial devices (sensors, actuators, equipment, etc.). Networking systems enable timely communication of data and control signals for diverse and dispersed tasks in industrial systems BIB008 . Computing systems provide the computational platform to enable timely collection, storage, processing, and analysis of data in an efficient, highly-reliable, and scalable manner BIB015 , BIB009 . Notice that the adoption of machine learning can be incorporated into all systems in I-IoT. Finally, the physical components are the manufacturing and automation systems. The primary contributions of this paper are three-fold: • I-IoT System Architecture, Physical Systems, and Requirements. We present the I-IoT architecture, which consists of three layers (i.e., application layer, communication layer, and physical layer). We introduce some typical I-IoT physical systems (e.g., PA and FA) and their characteristics (e.g., number of nodes, cycle time, and reliability). We also identify I-IoT performance requirements from the communications perspective (e.g., latency and reliability). • Control, Networking and Computing Systems. We conduct a survey of I-IoT from the three critical perspectives of control systems, networking systems, and computing systems. Concerning control systems, we investigate existing research efforts on industrial control systems, which play critical roles in the control and operation of industrial factories and plants. Those systems are categorized into centralized, decentralized, and hierarchical control systems. Regarding networking systems, we propose a three-dimensional framework to explore existing research efforts. We review some representative emerging networking technologies, including 5G, machine-to-machine (M2M) communication, and SDN for I-IoT. We also review some progress in the standardization of I-IoT. Considering computing systems, we propose a three-dimensional framework to investigate existing research efforts on computing in I-IoT. We review the state-of-the-art computing platforms for I-IoT, including cloud computing and hybrid cloud and edge-based computing. We also investigate the big data analysis supported by I-IoT computing systems. • Challenges and Future Directions We present the challenges and future research needs of control, networking and computing systems in I-IoT, as well as in machine learning for I-IoT. Particularly, we present the challenges in control system of enabling self-awareness, self-diagnosis and self-healing, efficient management, effective resource utilization, and timely maintenance. In addition, concerning networking systems, we discuss the problems of conducting network deployment, resource scheduling, and security. Moreover, regarding computing systems, we outline the challenges of the deployment of computing system, seamless integration, and computing resource management. In terms of future directions for control, networking, and computing systems, we outline several critical research needs such as co-design, intelligent data management and analysis, and develop a theoretical foundation, models and testbeds to allow design and analysis of these new types of systems. We further review recent progress on machine learning and illustrate an example of leveraging machine learning for networking in I-IoT. Finally, we outline research directions in machine learning for I-IoT, including machine learning for latencyguaranteed and ultra-reliable communications, machine learning-enabled cloud and edge computing, intelligent sensing and decision making, online learning and relearning, distributed machine learning, and light-weight learning platforms. The remainder of this paper is organized as follows: In Section II, we present the architecture of I-IoT, along with its industrial applications and characteristics. In Section III, we review control systems in I-IoT from the perspectives of centralized, distributed and hierarchical control. VOLUME 6, 2018 In Section IV, we review research efforts on networking systems in I-IoT and consider some representative techniques, such as 5G, M2M, and SDN. In Section V, we review the research efforts on computing systems in I-IoT, such as cloud computing, edge computing, and hybrid cloud and edge computing, among others. In Section VI, we present challenges, possible solutions and future directions in the areas of control, networking, and computing systems in I-IoT, as well as the use of machine learning in I-IoT. In Section VII, we conclude the paper. Notice that all main acronyms used in the paper and their descriptions are listed in Table 1 .