RESUMO

Industrial production and manufacturing systems require automation, reliability, as well as low-latency intelligent control. Industrial Internet of Things (IIoT) is an emerging paradigm that enables precise, low latency, intelligent computing, supported by cutting-edge technology such as edge computing and machine learning. IIoT provides some of the essential building blocks to drive manufacturing systems to the next level of productivity, efficiency, and safety. Hardware failures and faults in IIoT are critical challenges to be faced. These anomalies can cause accidents and financial loss, affect productivity, and mobilize staff by producing false alarms. In this context, this article proposes a framework called Detection and Alert State for Industrial Internet of Things Faults (DASIF). The DASIF framework applies edge computing to execute highly precise and low latency machine learning models to detect industrial IoT faults and autonomously enforce an adaptive communication policy, triggering a state of alert in case of fault detection. The state of alert is a pre-stage countermeasure where the network increases communication reliability by using data replication combined with multiple-path communication. When the system is under alert, it can process a fine-grained inspection of the data for efficient decison-making. DASIF performance was obtained considering a simulation of the IIoT network and a real petrochemical dataset.

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This paper proposes a new system based on the Industrial Internet of Things (IIoT) for the monitoring of Mobile Health (m-Health) of workers in the underground mining industry. The proposed architecture uses a hybrid model in data transmission. Visible Light Communication (VLC) is used for downlink because of its narrow coverage, which aids in worker positioning. Radio frequency (RF) communication technology is used to send data for primary vital signs in the uplink, which is more efficient in transmission and is a viable solution according to the problem raised. The results obtained in terms of coverage and transmission for the downlink and uplink links show the feasibility of implementing the proposed system.

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The Zero Trust concept is being adopted in information technology (IT) deployments, while human users remain to be the main risk for operational technology (OT) deployments. This article proposes to enhance the new Modbus/TCP Security protocol with authentication and authorization functions that guarantee security against intentional unauthorized access. It aims to comply with the principle of never trusting the person who is accessing the network before carrying out a security check. Two functions are tested and used in order to build an access control method that is based on a username and a password for human users with knowledge of industrial automation control systems (IACS), using simple means, low motivation, and few resources. A man-in-the-middle (MITM) component was added in order to intermediate the client and the server communication and to validate these functions. The proposed scenario was implemented using the Node-RED programming platform. The tests implementing the functions and the access control method through the Node-RED software have proven their potential and their applicability.

Assuntos

Segurança Computacional , Telemedicina , Humanos , Confidencialidade , Software

RESUMO

The Industrial Internet of Things (IIoT) is one of the most demanding IoT applications. The insertion of industries in the context of smart cities and other smart environments, allied with new communication technologies such as 5G, brings a new horizon of possibilities and new requirements. These requirements include low latency, the support of a massive quantity of devices and data, and the need to support horizontal communications between devices at the edge level. To make this feasible, it is necessary to establish an IIoT-to-cloud continuum distributing federated brokers across the infrastructure and providing scalability and interoperability. To attend this type of application, we present the Helix Multi-layered IoT platform and its operating modes. We report and discuss its real-world deployment in the Aveiro Tech City Living Lab in Aveiro, Portugal with functional and performance tests. We tested device-to-device communication across edge and core layers and also interconnected the infrastructure with one in São Paulo, Brazil, replicating the use of a global industry. The successful deployment validates the use of a Helix Multi-layered IoT platform as a suitable backend platform for IIoT applications capable of establishing the IIoT-to-cloud continuum. It also helps for the deployment of other applications in such a domain.

Assuntos

Internet das Coisas , Brasil , Cidades , Indústrias , Portugal

RESUMO

We report the experimental implementation of optically-powered wireless sensor nodes based on the power-over-fiber (PoF) technology, aiming at Industrial Internet of Things (IIoT) applications. This technique employs optical fibers to transmit power and is proposed as a solution to address the hazardous industrial environment challenges, e.g., electromagnetic interference and extreme temperatures. The proposed approach enables two different IIoT scenarios, in which wireless transmitter (TX) and receiver (RX) nodes are powered by a PoF system, enabling local and remote temperature data monitoring, with the purpose of achieving an intelligent and reliable process management in industrial production lines. In addition, the system performance is investigated as a function of the delivered electrical power and power transmission efficiency (PTE), which is the primary performance metric of a PoF system. We report 1.4 W electrical power deliver with PTE = 24%. Furthermore, we carry out a voltage stability analysis, demonstrating that the PoF system is capable of delivering stable voltage to a wide range of applications. Finally, we present a comparison of temperature measurements between the proposed approach and a conventional industrial programmable logic controller (PLC). The obtained results demonstrate that PoF might be considered as a potential technology to power and enhance the energy efficiency of IIoT sensing systems.

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The emergence of Industry 4.0 technologies, such as the Internet of Things (IoT) and Wireless Sensor Networks (WSN), has prompted a reconsideration of methodologies for network security as well as reducing operation and maintenance costs, especially at the physical layer, where the energy consumption plays an important role. This article demonstrates through simulations and experiments that, while the cooperative scheme is more efficient when a WSN is at normal operating conditions, the collaborative scheme offers more enhanced protection against the aggressiveness of jamming in the performance metrics, thus making it safer, reducing operation and maintenance costs and laying the foundations for jamming mitigation. This document additionally offers an algorithm to detect jamming in real time. Firstly, it examines the characteristics and damages caused by the type of aggressor. Secondly, it reflects on the natural immunity of the WSN (which depends on its node density and a cooperative or collaborative configuration). Finally, it considers the performance metrics, especially those that impact energy consumption during transmission.

RESUMO

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.

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In this paper, we propose a methodology to use the received signal strength indicator (RSSI) available by the protocol stack of an installed Wireless Sensor Network (WSN) at an electric-power-system environment (EPS) as a tool for obtaining the characteristic of its communication channel. Thereby, it is possible to optimize the settings and configuration of the network after its deployment, which is usually run empirically without any previous knowledge of the channel. A study case of a hydroelectric power plant is presented, where measurements recorded over a two-month period were analyzed and treated to obtain the large-scale characteristics of the radiofrequency channel at 2.4 GHz. In addition, we showed that instantaneous RSSI data can also be used to detect specific issues in the network, such as repetitive patterns in the transmitted power level of the nodes, and information about its environment, such as the presence of external sources of electromagnetic interference. As a result, we demonstrate the practical use of the RSSI long-term data generated by the WSN for its own performance optimization and the detection of particular events in an EPS or any similar industrial environment.

RESUMO

The Industrial Internet of Things (IIoT) is often presented as a concept that is significantly changing industry, yet continuous improvements in the identification and automation of objects are still required. Such improvements are related to communication speed, security, and reliability, critical attributes for industrial environments. In this context, the radio-frequency identification (RFID) systems present some issues related to frame collision when there are several tags transmitting data. The dynamic framed-slotted ALOHA (DFSA) is a widely used algorithm to solve collision problems in RFID systems. DFSA dynamically adjusts the frame length based on estimations of the number of labels that have competed for slots in the previous frame. Thus, the accuracy of the estimator is directly related to the label identification performance. In the literature, there are several estimators proposed to improve labels identification accuracy. However, they are not efficient when considering a large tag population, requiring a considerable amount of computational resources to perform the identification. In this context, this work proposes an estimator, which can efficiently identify a large number of labels without requiring additional computational resources. Through a set of simulations, the results demonstrate that the proposed estimator has a nearly ideal channel usage efficiency of 36.1%, which is the maximum efficiency of the DFSA protocol.