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The Industrial Internet of Things (IIoT) enables the integration of physical devices such as sensors and actuators into the virtual world of automation application systems via different communication protocols. Interoperability among the “things” appears to be one of the biggest conceptual and technological challenges in developing the IIoT framewo...
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... and integrate the sensor value into its respective service in the Arrowhead local cloud. The Analysis component exploits the SPARQL Query Engine to filter the data in the KB for 3 [Online]. Available: https://jena.apache.org/ only critical conditions about the monitoring services or applications and updates the querying results to the KB. Fig. 9 shows the SPARQL query that periodically checks the timestamp of the sensor observation and updates the status of the corresponding sensor service to OfflineState if the value is not updated within 20 seconds. As can be seen from this figure, the SAI ontology is also used as the language to describe the query. Furthermore, the Plan ...
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... In this recommendation, the key focus is on integrating network components such as IoT gateways, routers, and switches into existing control systems. This integration facilitates the transmission of data from IoT devices to a central system or the cloud, enabling remote monitoring and basic data analytics [76]. The core idea behind this recommendation is to enhance existing control systems with IIoT devices, empowering them to adapt operations in real time based on incoming data. ...
This research paper explores the financial adoption challenges of the Industrial Internet of Things (IIoT) in industry. Previous studies have mainly concentrated on designing affordable IIoT devices, reducing operational costs, and creating conceptual frameworks to assess the financial impact of IIoT adoption. The objective of this paper is to investigate whether IIoT adoption's financial benefits outweigh the initial costs in small and medium-sized enterprises (SMEs). The data from the Industrial Assessment Centers (IAC) database were analyzed, focusing on 62 U.S. manufacturing SMEs across 10 states and 25 Standard Industrial Classifications (SICs), evaluating projected IIoT implementation costs and anticipated cost savings. Results from the analyses reveal that statistically, the difference between implementation costs and savings is significant at a 95% confidence level. Practically, this indicates that SMEs, despite facing high initial costs, can expect these investments to be counterbalanced by substantial savings. From an engineering perspective, this finding raises awareness among SMEs that, beyond overcoming financial barriers, IIoT technologies serve as a strategic enhancement to operational efficiency and competitive positioning. This study acknowledges the limitations including reliance on estimated projections and a narrow industry focus. Future research should broaden the sample and explore the lifecycle costs of IIoT.
... The authors have showed the novelty and effectiveness of the proposed system as compare to traditional scheme. Lam et al. [24] have proposed a decentralized automatic orchestration and configuration mechanism based upon semantic policies. The proposed approach was deployed and verified while sending the information during planning and production in IIoT environment while transmitting over cloud. ...
Industrial Internet-of-Things (IIoT) is a powerful IoT application which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this paper, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the malicious devices to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
With the rise of Industry 4.0, its pillars (which include Internet of Things, “Big Data”, data analytics, augmented reality, cybersecurity, etc.) have become unavoidable tendencies for the automated manufacturing industry. Equipment upgrade is required to match the new standards of digitally assisted automation. However, not all factories in the medium to small range (or independent manufacturers) can afford to upgrade their equipment. Therefore, the availability of affordable Industry 4.0 upgrades for now-outdated devices is necessary for manufacturers in the SME range (Small-Medium Enterprises) to stay relevant and profitable. More specifically, this work revolves around the automation of printed circuit board (PCB) manufacturing, which is one of the most popular and profitable areas involved in this movement; and within it, the large majority of manufacturing defects can be traced to the soldering or “reflow” stage. Manufacturing research must, thus, aim towards improving reflow ovens and, more specifically, aim to improve their autonomous capabilities and affordability. This work presents the design and results of a controlling interface utilizing a Raspberry Pi 4 as a coupling interface between an MQTT Broker (which monitors the overall system) and the oven itself (which is, intentionally, a sub-prime model which lacks native IoT support), resulting in successful, remote, network-based controlling and monitoring of the oven. Additionally, it documents the design and implementation of the network adaptations necessary for it to be considered a cybersecure IIoT Module and connect safely to the Production Cell’s Subnet. All of this to address the inclusion of specific Industry 4.0 needs such as autonomous functioning, data collection and cybersecurity in outdated manufacturing devices and help enrich the processes of SME PCB manufacturers.
Integrating IoT and Cloud computing technologies attains efficient solutions for quality insurance. The combination of both technologies with the autonomic computer creates powerful mechanisms to deal with abnormal situations. The state-of-the-art solvers combined then with unbalanced functionalities using top-down updating for device controlling and bottom-up updating to inform the IoT-Cloud platform about the captured contextual information by the device. This solution reduces the benefits of horizontal collaborations on both sides. For that, we propose a novel platform cooperation based on the CIoTAS protocol to enhance the contextual decisions of IoT devices. The proposed approach enriches the knowledge of Cloud platforms and explores the capacity of IoT devices (things) based on their awareness by updating their data based on the changes in the behavior and context of IoT devices. We proved the protocol correctness according to the liveness and closure properties. We conducted a simulation part to illustrate and clarify the efficiency of our proposal.
Industrial Internet-of-Things (IIoT) is a powerful IoT application, which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places, and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this article, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the MD to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.
