E. Goldin

Bio: E. Goldin is an academic researcher. The author has contributed to research in topics: Process control & Big data. The author has an hindex of 1, co-authored 1 publications receiving 14 citations.

Cloud computing for big data analytics in the Process Control Industry

Abstract: The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0.

Edge Powered Industrial Control: Concept for Combining Cloud and Automation Technologies

Abstract: In the past, industrial control of field devices was comprised of self-contained systems in a dedicated network for exchanging control information between field devices and control hardware to accomplish process tasks. Nowadays, cloud computing enables a massive amount of computing resources and high availability, which opens up new potentials in the industrial sector. Until now, the integration of cloud solutions in industrial control was limited due to missing technologies connecting the Internet of Things with industrial requirements. Furthermore, based on existing paradigms there is a lack of appropriate architecture concepts for industrial control. This paper depicts a platform concept, which combines cloud computing and industrial control using edge devices realized for an automation cell.

Characteristic insights on industrial cyber security and popular defense mechanisms

Abstract: Due to the deep integration of information technology and operational technology, networked control systems are experiencing an increasing risk of international cyber attacks In practice, industrial cyber security is a significant topic because current networked control systems are supporting various critical infrastructures to offer vital utility services By comparing with traditional IT systems, this paper first analyzes the uncontrollable cyber threats and classified attack characteristics, and elaborates the intrinsic vulnerabilities in current networked control systems and novel security challenges in future Industrial Internet After that, in order to overcome partial vulnerabilities, this paper presents a few representative security mechanisms which have been successfully applied in today's industrial control systems, and these mechanisms originally improve traditional IT defense technologies from the perspective of industrial availability Finally, several popular security viewpoints, adequately covering the needs of industrial network structures and service characteristics, are proposed to combine with burgeoning industrial information technologies We target to provide some helpful security guidelines for both academia and industry, and hope that our insights can further promote in-depth development of industrial cyber security

The architecture and key technologies for an industrial Internet with synergy between the cloud and clients

Abstract: The fourth industrial revolution has been unveiled with the rapid development of Internet of things (IoT), cloud computing, and big data. The industrial Internet, as a highly cooperative and intelligence-sharing global network that connects entities, human beings, and the environment in smart manufacturing, is the core of this revolution. However, most current research on the industrial Internet is restricted to IoT, cloud computing, or big data, respectively. The synergy between the cloud and clients is currently at a very primary stage of sensing, connection, and knowledge, lacking a cloud-client-integrative architecture and key technologies that could meet the evolving requirements of networked smart production, including more complex objects to be sensed, more diversification of entities to be connected, faster data processing, and more intelligent feedback control. This paper first surveys some important research directions with respect to this research field and summarizes the research status and challenges. On this basis, a novel cloud-client integrative industrial Internet architecture and solutions for related key technologies are proposed. Then, the proposed technologies are demonstrated for some specific applications in the field of intelligent manufacturing. Finally, the prospects for cloud-client-integrative industrial Internet research are discussed and concluded.

A low-cost cloud-extended sensor network for supervisory control

Abstract: The current automation supervisory control systems are situated in well-restricted areas and require investments in computing hardware and communication systems. In machine automation systems, any additional computing hardware can be cumbersome to install, making upgrades hard to apply. This paper presents a cloud-extended sensor network with supervisory control in a public cloud. The hardware and cloud resources used in the solution are low-cost, reducing the up-front costs compared to the use of high-end components. The system collects data from ST microprocessor (STM)-based sensor nodes that send inertial measurement data using user datagram protocol (UDP). The sensor itself is a Bosch BMI160, a cheap and small inertial measurement unit (IMU). The system is designed to be used in machine automation applications where the frequency of the sensory data produced is hundreds of hertz. The system is to provide low-latency data transfer to the cloud. In the cloud environment, data is collected by a computing service that can be programmed to perform algorithms on it. The system is tested in a setup consisting of five IMU sensors and an angle measurement unit attached to a hydraulically actuated flexible beam. The test setup aims to update a local control system's parameters based on a cloud algorithm and camera measurements of the beam tip position. The control results and communication latency are inspected. The main advantages of the proposed solution are the simple system architecture and cost savings with the use of low-cost sensors and cloud resources. The focus of this study is the functionality of such a system; intricate security issues are beyond the scope of this study.

Agni: An Efficient Dual-access File System over Object Storage

Abstract: Object storage is a low-cost, scalable component of cloud ecosystems. However, interface incompatibilities and performance limitations inhibit its adoption for emerging cloud-based workloads. Users are compelled to either run their applications over expensive block storage-based file systems or use inefficient file connectors over object stores. Dual access, the ability to read and write the same data through file systems interfaces and object storage APIs, has promise to improve performance and eliminate storage sprawl. We design and implement Agni1, an efficient, distributed, dual-access object storage file system (OSFS), that uses standard object storage APIs and cloud microservices. Our system overcomes the performance shortcomings of existing approaches by implementing a multi-tier write aggregating data structure and by integrating with existing cloud-native services. Moreover, Agni provides distributed access and a coherent namespace. Our experiments demonstrate that for representative workloads Agni improves performance by 20%--60% when compared with existing approaches.