Manufacturing industry profoundly impact economic and societal progress. As being a commonly accepted term for research centers and universities, the Industry 4.0 initiative has received a splendid attention of the business and research community. Although the idea is not new and was on the agenda of academic research in many years with different perceptions, the term “Industry 4.0” is just launched and well accepted to some extend not only in academic life but also in the industrial society as well. While academic research focuses on understanding and defining the concept and trying to develop related systems, business models and respective methodologies, industry, on the other hand, focuses its attention on the change of industrial machine suits and intelligent products as well as potential customers on this progress. It is therefore important for the companies to primarily understand the features and content of the Industry 4.0 for potential transformation from machine dominant manufacturing to digital manufacturing. In order to achieve a successful transformation, they should clearly review their positions and respective potentials against basic requirements set forward for Industry 4.0 standard. This will allow them to generate a well-defined road map. There has been several approaches and discussions going on along this line, a several road maps are already proposed. Some of those are reviewed in this paper. However, the literature clearly indicates the lack of respective assessment methodologies. Since the implementation and applications of related theorems and definitions outlined for the 4th industrial revolution is not mature enough for most of the reel life implementations, a systematic approach for making respective assessments and evaluations seems to be urgently required for those who are intending to speed this transformation up. It is now main responsibility of the research community to developed technological infrastructure with physical systems, management models, business models as well as some well-defined Industry 4.0 scenarios in order to make the life for the practitioners easy. It is estimated by the experts that the Industry 4.0 and related progress along this line will have an enormous effect on social life. As outlined in the introduction, some social transformation is also expected. It is assumed that the robots will be more dominant in manufacturing, implanted technologies, cooperating and coordinating machines, self-decision-making systems, autonom problem solvers, learning machines, 3D printing etc. will dominate the production process. Wearable internet, big data analysis, sensor based life, smart city implementations or similar applications will be the main concern of the community. This social transformation will naturally trigger the manufacturing society to improve their manufacturing suits to cope with the customer requirements and sustain competitive advantage. A summary of the potential progress along this line is reviewed in introduction of the paper. It is so obvious that the future manufacturing systems will have a different vision composed of products, intelligence, communications and information network. This will bring about new business models to be dominant in industrial life. Another important issue to take into account is that the time span of this so-called revolution will be so short triggering a continues transformation process to yield some new industrial areas to emerge. This clearly puts a big pressure on manufacturers to learn, understand, design and implement the transformation process. Since the main motivation for finding the best way to follow this transformation, a comprehensive literature review will generate a remarkable support. This paper presents such a review for highlighting the progress and aims to help improve the awareness on the best experiences. It is intended to provide a clear idea for those wishing to generate a road map for digitizing the respective manufacturing suits. By presenting this review it is also intended to provide a hands-on library of Industry 4.0 to both academics as well as industrial practitioners. The top 100 headings, abstracts and key words (i.e. a total of 619 publications of any kind) for each search term were independently analyzed in order to ensure the reliability of the review process. Note that, this exhaustive literature review provides a concrete definition of Industry 4.0 and defines its six design principles such as interoperability, virtualization, local, real-time talent, service orientation and modularity. It seems that these principles have taken the attention of the scientists to carry out more variety of research on the subject and to develop implementable and appropriate scenarios. A comprehensive taxonomy of Industry 4.0 can also be developed through analyzing the results of this review.

Literature review of Industry 4.0 and related technologies

Sending data to the cloud for analysis was a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.

A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet

Emerging network computing technologies extend the functionalities of industrial IoT (IIoT) terminals. However, this promising service-provisioning scheme encounters problems in untrusted and distributed IIoT scenarios because malicious service providers or clients may deny service provisions or usage for their own interests. Traditional nonrepudiation solutions fade in IIoT environments due to requirements of trusted third parties or unacceptable overheads. Fortunately, the blockchain revolution facilitates innovative solutions. In this paper, we propose a blockchain-based fair nonrepudiation service provisioning scheme for IIoT scenarios in which the blockchain is used as a service publisher and an evidence recorder. Each service is separately delivered via on-chain and off-chain channels with mandatory evidence submissions for nonrepudiation purpose. Moreover, a homomorphic-hash-based service verification method is designed that can function with mere on-chain evidence. And an impartial smart contract is implemented to resolve disputes. The security analysis demonstrates the dependability, and the evaluations reveal the effectiveness and efficiency.

A Blockchain-Based Nonrepudiation Network Computing Service Scheme for Industrial IoT

With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era. A vast amount of research work has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, very few research work provides a complete survey of the whole pine-line of the multimedia big data analytics, including the management and analysis of the large amount of data, the challenges and opportunities, and the promising research directions. To serve this purpose, we present this survey, which conducts a comprehensive overview of the state-of-the-art research work on multimedia big data analytics. It also aims to bridge the gap between multimedia challenges and big data solutions by providing the current big data frameworks, their applications in multimedia analyses, the strengths and limitations of the existing methods, and the potential future directions in multimedia big data analytics. To the best of our knowledge, this is the first survey that targets the most recent multimedia management techniques for very large-scale data and also provides the research studies and technologies advancing the multimedia analyses in this big data era.

Multimedia Big Data Analytics: A Survey

Challenges and recommended technologies for the industrial internet of things: A comprehensive review

Secure data aggregation schemes are widely adopted in wireless sensor networks, not only to minimize the energy and bandwidth consumption, but also to enhance the security. Statistics obtained from data aggregation schemes often fall into three categories, i.e., distributive, algebraic, and holistic. In practice, a wide range of reasonable aggregation queries are combinations of several different statistics. Providing multi-functional aggregation support is also a primary demand for data preprocessing in data mining. However, most existing secure aggregation schemes only focus on a single type of statistics. Some statistics, especially holistic ones (e.g., median), are often difficult to compute efficiently in a distributed mode even without considering the security issue. In this paper, we first propose a new Multi-functiOnal secure Data Aggregation scheme (MODA), which encodes raw data into well-defined vectors to provide value-preservation, order-preservation and context-preservation, and thus offering the building blocks for multi-functional aggregation. A homomorphic encryption scheme is adopted to enable in-ciphertext aggregation and end-to-end security. Then, two enhanced and complementary schemes are proposed based on MODA, namely, RandOm selected encryption based Data Aggregation (RODA) and COmpression based Data Aggregation (CODA). RODA can significantly reduce the communication cost at the expense of slightly lower but acceptable security on a leaf node, while CODA can dramatically reduce communication cost with the lower aggregation accuracy. The performance results obtained from theoretic analysis and experimental evaluation of three real datasets under different scenarios, demonstrate that our schemes can achieve the performance superior to the most closely related work.

Multi-functional secure data aggregation schemes for WSNs

The compressive sensing (CS) based data collection schemes can effectively reduce the transmission cost of wireless sensor networks (WSNs) by exploring the sparsity of compressible signals. Although many recent works explained CS as a symmetric cryptosystem, CS-based data collection schemes still face security threats, due to the complex deployment environment of WSNs. In this paper, we first propose two feasible attack models for specific applications. Then, we present a se cure d ata c ollection scheme based on compressive sensing (SeDC), which enhances the data privacy by the asymmetric semi-homomorphic encryption scheme, and reduces the computation cost by sparse compressive matrix. More specifically, the asymmetric mechanism reduces the difficulty of secret key distribution and management. The homomorphic encryption allows the in-network aggregation in cipher domain, and thus enhances the security and achieves the network load balance. The sparse measurement matrix reduces both the computation cost and communication cost, which compensates the increasing cost caused by the homomorphic encryption. We also introduce a joint recovery model to improve the recovery accuracy. Experimental evaluation based on real data shows that the proposed scheme achieves a better performance compared with the most related works.

A secure data collection scheme based on compressive sensing in wireless sensor networks

CSF: Crowdsourcing semantic fusion for heterogeneous media big data in the internet of things

With the emergence of cloud computing, big data, and mobile networks, the computing paradigm and related technologies have experienced significant development over the past 10 years. Concomitantly, the prevalence of smartphones, wearable devices, and mobile applications has been constantly changing our daily lives. Terminals are evolving toward lightweight, intelligent, highly secure, and convenient devices. As an emerging computing paradigm, cloud computing focuses primarily on providing services through servers and networks but without addressing the inherent challenges and concerns of user terminals, such as energy efficiency, security, and cross-platform compatibility. Consequently, these challenges remain in the era of cloud computing and big data. In this article, the authors present a review to a promise computing paradigm: transparent computing. Similar to cloud computing, transparent computing stores software and user data at specific servers. More specifically, it extends the bus transmission in traditional computer architecture to the network. User interruptions at a terminal are redirected to a specific server through a network connection to request the corresponding instructions and data, which are subsequently executed at the terminal in a page-streaming pattern. By adopting this computing paradigm, user terminals are becoming more lightweight (nearly bare) with enhanced security (dumping after using), improved energy efficiency (no terminal storage), and cross-platform capability (low layer compatibility). This article presents a comprehensive survey and indicates future directions of transparent computing, from traditional terminals to mobile devices.

Kehua Guo

Papers

Multi-functional secure data aggregation schemes for WSNs

A secure data collection scheme based on compressive sensing in wireless sensor networks

CSF: Crowdsourcing semantic fusion for heterogeneous media big data in the internet of things

Transparent Computing: A Promising Network Computing Paradigm

An effective and economical architecture for semantic-based heterogeneous multimedia big data retrieval