The FORA Fog Computing Platform for Industrial IoT

Pop, Paul; Zarrin, Bahram; Barzegaran, Mohammadreza; Schulte, Stefan; Punnekkat, Sasikumar; Ruh, Jan; Steiner, Wilfried

doi:10.1016/j.is.2021.101727

Cited by 60 publications

(36 citation statements)

References 81 publications

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“…Fog computing paradigm (FCP) is used for industrial IoT applications, where time-critical and deterministic networking is required. One of the models proposed a system [148] based on fog computing platform for the industrial applications using Architecture Analysis Design Language (AADL) [149], to analyze the suitability of FCP in industrial automation. AADL is used to model the software and hardware for embedding systems for real-time applications.…”

Section: Mud In Fog Computingmentioning

confidence: 99%

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

et al. 2021

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This paper presents an overview of device identification techniques and the Manufacturer Usage Description (MUD) standard used for the Internet of things to reduce the IoT attack surface. The ongoing diversity and the sheer increase in the number of connected IoT devices have crumpled security efforts. There is a need to reconsider and redesign the underlying concept of developing security systems to resolve IoT security challenges. In this backdrop, device profiling and identification have emerged as an exciting technique that helps to reduce IoT device attack surface. One of the known approaches for device identification is to fingerprint a device. There are many ways to fingerprint the device, mostly using device network flows or device local attributes. The device identification ensures the authenticity of the device attached to the network, like user authentication. Since IoT devices mostly work using machine-to-machine (M2M) communication, this requires identifying each device properly. But there is no unified approach for device identification for the ever-growing world of IoT devices and applications. One of the major steps forward in this direction is the development of the Manufacturer Usage Description (MUD) standard that defines the role of a device within the network. It limits the device to execute the primary task only, which will help to reduce the attack surface. Since the inception of MUD, many security frameworks use this standard for IoT security. However, there is a need to scrutinize the security frameworks based on the MUD, to find out the claimed effectiveness of the standard in IoT security. This paper initially identifies and classifies the potential vulnerabilities in IoT devices. Then, the study provides an overview of the research that focuses on device identification techniques and analyzes their role in IoT security. Finally, the research presents an overview of MUD technology, its implementation scenarios, the limitation of the latest MUD standard, and its applications in the industry. The prime aim of this work is to examine the MUD benefits in IoT security along with the weaknesses and challenges while implementing this standard along with future directions. INDEX TERMSManufacturer usage description (MUD), Internet of Things (IoT), device identification (DI), software defined network (SDN), machine learning (ML), deep learning (DL). NOMAN MAZHAR received the B.E. degree in software engineering and the M.S. degree in information technology from the

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Section: Mud In Fog Computingmentioning

confidence: 99%

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

et al. 2021

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“…Through the I-IoT, all kinds of equipment in the production site can work together [ 20 ]. Edge computing nodes can provide lower latency computing services because they are closer to sensors and actuators than cloud servers [ 21 ].…”

Section: Overviewmentioning

confidence: 99%

Data Sharing Mechanism of Sensors and Actuators of Industrial IoT Based on Blockchain-Assisted Identity-Based Cryptography

Meng

2021

Sensors

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The Industrial IoT is one of the key technologies to improve industrial production efficiency. The entire production process usually involves multiple production regions and numerous smart devices (sensors and actuators). The efficiency of the Industrial IoT is limited by this strong coupling relationship between the subsystem and the sensors and actuators. In this paper, to unleash the potential of Industrial IoT, a safe and reliable data sharing mechanism of sensors and actuators is proposed. We deployed distributed identity authentication and data proxy services in various regions. In the device authentication process, we used identity-based encryption algorithms, and we solved the trust problem between different regions by introducing a private blockchain. In addition, we designed the model of device capability (MDC) to describe the device, enabling it to be shared with a standard interface. Finally, we conducted many performance tests on the proposed mechanism. The test results verified the effectiveness and efficiency of the proposed mechanism.

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“…This section describes our proposed fogified drive architecture, which is based on the FORA FCP [15]. We assume that using our proposed fogified architecture, drives are developed as FNs that are connected to each other and to the machines through TSN [51].…”

Section: Fogified Drive Architecturementioning

confidence: 99%

“…These resources are shared for running applications that sit on the software component. Similar to [15], we consider a fog-based design that uses TSN because it supports mixed-criticality bounded-latency communication via multiple traffic types. For the scheduled traffic, which requires synchronized schedule tables, TSN employs a network-wide clock synchronization protocol, namely, IEEE 802.1AS [57] with sub-microsecond precision.…”

Section: Fogified Drive Architecturementioning

confidence: 99%

Electric drives as fog nodes in a fog computing‐based industrial use case

Barzegaran

Desai

Qian

et al. 2021

The Journal of Engineering

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Electric drives, which are a main component in industrial applications, control electric motors and record vital information about their respective industrial processes. The development of electric drives as Fog nodes within a fog computing platform (FCP) leads to new abilities such as programmability, analytics, and connectivity, increasing their value. In this study, the FORA FCP reference architecture is used to implement electric drives as Fog nodes, which is called "fogification". The fogified drive architecture and its components are designed using Architecture Analysis and Design Language (AADL). The design process was driven by the high-level requirements that the authors elicited. Both the fogified drive architecture and the current drive architecture are used to implement a self baggage drop system in which electric drives are the key components. The fog-based design was then evaluated using several key performance indicators (KPIs), which reveal its advantages over the current drive architecture. The evaluation results show that safety-related isolation is enabled with only 9% overhead on the total Fog node utilization, control applications are virtualized with zero input-output jitter, the hardware cost is reduced by 44%, and machine learning at the edge is performed without interrupting the main drive functionalities and with an average 85% accuracy. The conclusion is that the fog-based design can successfully implement the required electric drive functionalities and can also enable innovative uses needed for realizing the vision of Industry 4.0. INTRODUCTIONIndustry 4.0 is an industrial revolution via digitalization that affects all industries and sectors. Digitization increases productivity, flexibility, and product quality. Moreover, it reduces time-to-market and supports mass-customization [1]. When machines become connected with each other, sensors, and actuators, they form the Industrial IoT (IIoT), which is expected to increase the global gross domestic product (GDP) value to USD 15 trillion by 2030 [2]. The convergence of Operational Technology (OT) and Information Technology (IT) drives this digital transformation [3]. However, OT and IT use different computation and communication technologies [3]. OT employs dedicated hardware and software to implement real-time and safety-critical applications that have stringent timing and dependabilityThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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The FORA Fog Computing Platform for Industrial IoT

Cited by 60 publications

References 81 publications

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

Role of Device Identification and Manufacturer Usage Description in IoT Security: A Survey

Data Sharing Mechanism of Sensors and Actuators of Industrial IoT Based on Blockchain-Assisted Identity-Based Cryptography

Electric drives as fog nodes in a fog computing‐based industrial use case

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