A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> E. BIG DATA ANALYSIS <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> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> E. BIG DATA ANALYSIS <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> BIB002 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> E. BIG DATA ANALYSIS <s> The Industrial Internet of Things (IIoT) is quite different from the general IoT in terms of latency, bandwidth, cost, security and connectivity. Most existing IoT platforms are designed for general IoT needs, and thus cannot handle the specificities of IIoT. With the anticipated big data generation in IIoT, an open source platform capable of minimizing the amount of data being sent from the edge and at the same time, that can effectively monitor and communicate the condition of the large-scale engineering system by doing efficient real-time edge analytics is sorely needed. In this work, an industrial machine condition-monitoring open-source software database, equipped with a dictionary and small enough to fit into the memory of edge data-analytic devices is created. The database-dictionary system will prevent excessive industrial and smart grid machine data from being sent to the cloud since only fault report and requisite recommendations, sourced from the edge dictionary and database will be sent. An open source software (Python SQLite) situated on Linux operating system is used to create the edge database and the dictionary so that inter-platform portability will be achieved and most IIoT machines will be able to use the platform. Statistical analysis at the network edge using well known industrial methods such as kurtosis and skewness reveal significant differences between generated machine signal and reference signal. This database-dictionary approach is a new paradigm since it is different from legacy methods in which databases are situated only in the cloud with huge memory and servers. The open source deployment will also help to satisfy the criteria of Industrial IoT Consortium and the Open Fog Architecture. <s> BIB003 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> E. BIG DATA ANALYSIS <s> Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle. <s> BIB004 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> E. BIG DATA ANALYSIS <s> Cyber-Physical Systems (smart grid, smart transportation, smart cities, etc.), driven by advances in Internet of Things (IoT) technologies, will provide the infrastructure and integration of smart applications to accelerate the generation and collection of big data to an unprecedented scale. As now a fundamental commodity in our current information age, such big data is a crucial key to competitiveness in modern commerce. In this paper, we address the issue of privacy preservation for data auction in CPS by leveraging the concept of homomorphic cryptography and secured network protocol design. Specifically, we propose a generic Privacy-Preserving Auction Scheme (PPAS), in which the two independent entities of Auctioneer and Intermediate Platform comprise an untrusted third-party trading platform. Via the implementation of homomorphic encryption and one-time pad, a winner in the auction process can be determined and all bidding information is disguised. Yet, to further improve the security of the privacy-preserving auction, we additionally propose an Enhanced Privacy-Preserving Auction Scheme (EPPAS) that leverages an additional signature verification mechanism. The feasibilities of both schemes are validated through detailed theoretical analyses and extensive performance evaluations, including assessment of the resilience to attacks. In addition, we discuss some open issues and extensions relevant to our scheme. <s> BIB005 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> E. BIG DATA ANALYSIS <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> BIB006
Big data in I-IoT refers to the huge volume, velocity, veracity, etc. of data collected from substantial volume of industrial sensors, actuators and devices. Enabling big data sharing is important and the key techniques include the design trading platforms and algorithms BIB004 , BIB005 . From a data analytics point of view, the available computing resources play a fundamental role in massive and complex data analysis. Particularly, I-IoT envisions the situational awareness, diagnosis, self-healing and prediction of industrial processes, which in turn requires powerful data analysis capabilities BIB002 , BIB001 . The big data generated by I-IoT systems is heterogeneous in size, volume, velocity, delay and reliability requirements. Thus, it is critical to leverage both time-efficient edge computing and computationally powerful cloud computing to process, store, and retrieve data based on its performance requirements (i.e., time-sensitive and non-time-sensitive). To this end, Fu et al. BIB006 proposed a framework that consists of five entities (I-IoT, edge server, proxy server, cloud server, and data users). In their architecture, the edge server is deployed to extracts and processes data that is time-sensitive data (e.g., control information) so that timely control decisions can be enabled. The proxy server encrypts the data, and the encrypted data and index structure are outsourced to the cloud. The cloud server is also used for data storage and interacting with data users. This work can be mapped to <Storage, PA/FA, QoS> in Figure 10 . Other research efforts have leveraged big data analysis to improve the efficiency of I-IoT. For example, in the monitoring of large-scale industrial systems, we can preprocess collected data in edge devices and upload only critical fault reports and important information for cloud storage and control decisions. In this direction, Oyekanlu BIB003 proposed a light-weight database with a small-size dictionary stored from the fully functional monitored machines (motor, turbines, etc.) on edge devices to improve the system efficiency. The edge devices are used to compute and analyze the differences between the machine signals and reference signals, and send only the alert signals and requisite recommendations to the operation center. This work can be mapped into cube <Transmission, FA, Others> in Figure 10 .