In this paper, an AI tool based on a Large Language Model is presented to generate the mandatory TEDS of the IEEE 1451 standard. The TEDS generation process is a challenging process that requires a comprehensive knowledge of the IEEE 1451 and TEDS format. Currently, the process is manual, with entering the required data and choosing a TEDS template. However, in order to simplify the process, an AI tool is proposed to receive the user’s data in a simple text entry format and generate the binary codes automatically. This tool is presented in this paper along with some examples to demonstrate the feasibility of the process.

In this paper, a new model for the design and development of distributed control and measurement systems will be presented. The system is based on IEC 61499 and IEEE 1451 standards to benefit from the features they offer. The robust distributed control mechanism of IEC 61499 compliments the advanced plug-and-play feature of IEEE 1451 at the transducer level. The proposed employs the Unified Modelling Language (UML) for an earthquake detection system as a case study. System requirements, design and development, and implementation are the three phases of the model at two levels of Cyber and Physical. This will allow the user to visualize the whole system that can be adopted for any application. Several UML diagrams are used in order to facilitate the design process at different levels of abstraction.

Distributed control and measurement systems are robust and flexible thanks to their reconfigurability and plug-and-play characteristics. IEC 61499 and IEEE 1451 standards are proposed for distributed and plug-and-play control and measurement systems. However, IEC 61499 standards do not provide an integration guideline at the transducer level, and at the same time, IEEE 1451 lacks a robust control mechanism similar to the one IEC 61499 provides. For these reasons, introducing the Transducer Interface Module defined by IEEE 1451 will enhance the reconfigurability and plug-and-play characteristics of the IEC 61499 at the sensors and actuators level. This paper presents the integration phase of a Transducer Interface Module into the IEC 61499 standard. A temperature-controlling system, which is fully IEC 61499- and IEEE 1451-compliant, is implemented and discussed.

Interoperability is an essential key in the Industrial Internet of Things and Industry 4.0, enabling devices and equipment from different vendors to share information and knowledge. Levels of interoperability go from a simple connection between devices to complex semantic levels of connectivity. Semantic connectivity enables information sharing between systems with unambiguous meaning, which is ideal for communication in the industry. It can solve interoperability issues, tool development, and service implementation. A reference architecture model presents an architecture to solve the interoperability problem focusing on standards to allow the connections between the devices and systems. This work presents a development of the proposed ontology developed based on the IEEE 1451 family of standards. The final result finds applications solving semantic level issues, adding linked data, and enabling interoperability with or without a reference architecture model.

This paper focuses on the interoperability of smart devices (sensor and actuators) in the context of the Industrial Internet of Things. IEC 61499 and IEEE 1451 standards are employed to implement a distributed control and measurement system. It addresses how two standards can interoperate and communicate efficiently by complementing each other. Both standards share common properties which are the essential elements of any IoT system. The Message Queue Telemetry Protocols (MQTT) is utilized to provide an interoperable network interface between the IEEE 1451 family of standards and the IEC 61499 standard. An interoperability test platform is provided to show how both standards communicate efficiently for a simple read/ write automation process.

Interoperability is one of the important characteristics of the Internet of Things. The interoperability enables smart things to exchange data in different areas with different levels of interoperability. Industrial Internet of Things is one of them, and it has the focus in the manufacturing process and connected with other associated ideas as Industry 4.0, Cyber-Physical Systems, and Cyber-Physical Production Systems. It is mandatory to define reference architectures models to manage the Industry 4.0 and the Industrial Internet of Things. The reference models enable semantic interoperability. A reference model provides a bottom/top view of an industrial process, going from the business to the physical layer. At the physical layer, it is necessary to combine physical things with digital world representations, extending its functionalities in the manufacturing process. IEEE 1451 family standards has the essential characteristics to support the exchange of information between IoT applications and sensors & actuators, allowing the industry to build digital elements and meets the Industry 4.0 requirements. This paper presents the reference models proposed for Industry 4.0, taking as reference the implementation and adoption of the IEEE 1451 standard as an option to acquire data and communicates inside of an industrial process. However, at this point, IEEE 1451 enables to achieve the syntactic level of interoperability, in this paper is discussed how to achieve the semantic level of interoperability using the IEEE 1451 family of standards.

As the smart factories become more and more connected, the wireless connectivity of the production and material handling machines and systems become increasingly common in many factory floors. It is hence important to question the impacts of Industrial Wireless Sensor Networks in the performance of smart factories, and in particular their end-to-end reliability. One of the most challenging problems is to determine optimal positions for mounting the wireless sensor nodes for high quality signal reception. Base station placement has a significant impact on network lifetime performance for a wireless sensor network. This problem is particularly challenging in harsh factory environments due to its coupling with data routing. Thus the position and total number of base stations used affect the reliability of the sensor networks used in connecting the factory. This paper presents an approximation technique based on K-means clustering method to determine an optimal position for placing base stations, and minimum number of base stations required for a smart factory implementation. The proposed approach has been validated by simulations, which suggest that increasing the number of the base stations will enhance the communication link quality in industrial environments where there is a huge effect of fading and shadowing. This paper focuses on the problem of placement for base stations in a scale smart factory model where the wireless nodes are used for communication between a number of connected automated manufacturing and material handling equipment.

Technological advancements are allowing the development of smart sensing devices with extremely low energy requirements. An emerging trend is the powering of smart sensors with energy harvested from the operating environment. Simultaneously, the task performed by a sensing node can be performed by a microprocessor with low computational resources. Energy demand of the devices is also decreasing by the adoption of advanced operating methods and energy management strategies. For devices to interoperate efficiently together, the design and operation need to follow standardization methods.

This paper presents design and implementation of a real-time monitoring and troubleshooting system using Wireless Sensor Networks (WSN) technology operating based on LoRa protocol. The main focus in this study was given to applications in industry, where most of the machinery are conventional and do not support modern digital data communication technologies common for factory networking. The general procedure and the details of implementation are presented through a prototype implementation performed on production machines of a food-processing plant. Proposed system consists of LoRA-based wireless motes that record the variations in operating temperature and vibrations on production machines through sensors and, wirelessly report summary data in real-time to a developed monitoring software interface for system diagnostics, troubleshooting and preventative maintenance.

Nowadays biometric identification systems are widely spread since the safety of those systems has been proven. They exhibit a large number of advantages when compared to other identification systems such as key and password that are subject to falsification and loss. Among biometric systems, finger vein recognition based on venous network has been considered recently in the literatures. This paper aims to present a finger vein recognition system using Support Vector Machine (SVM) based on a supervised training algorithm. The proposed system is divided in several phases, each performing a specific task. Two pre-processing schemes are employed in order to assess the efficiency in terms of recognition rate. Simulation results show that using Gabor filters in preprocessing for codifying the venous network and SVM for the classification can improve the recognition rate when compared to the existing methods.

The usage and deployment of Wireless Sensor Networks (WSN) is rapidly increasing in many different monitoring and control applications. In the majority of these applications, energy is a key factor in sensor networks since the sensor nodes are battery powered and hence have limited resources of energy. In this context, choosing a proper energy-efficient routing technique can increase the network life time. In this paper, a new Hybrid Energy-Efficient (HEE) routing protocol is proposed. HEE uses Direct Transmission (DT) and Minimum Energy Transmission (MTE) which are two of the simplest methods in terms of computational complexity. However the design of routing techniques is highly dependent on the application and the performance may vary based on environmental parameters. The novel proposed method is applicable for different networks regardless of the size and distances between the nodes and also with different parameters such as number of nodes and message length. Simulation results show how HEE performs more efficiently in terms of energy consumption when comparing to DT and MTE.

Wireless Sensor Networks are an emerging technology which has been recently adopted in many applications. Due to its wireless nature, the analysis of the radio propagation models plays an important role for performance evaluation in both theoretical and practical aspects. In this regards, path loss exponent is one of the most important parameter which has been considered widely in wireless communications analysis. There are several theoretical evaluations of path loss exponent for wireless sensor networks available in the literature. However there is a lack of experimental evaluation of both path loss exponent and the effect of shadowing. In this paper, three environments (free space, in building and industrial), where wireless sensor nodes are widely deployed, have been chosen in order to evaluate the experimental analysis. Path loss and path loss exponent are measured by means of Received Signal Strength Indicator (RSSI) and based on them, the standard deviation of shadowing effect is also calculated. All the measured parameters are compared with the theoretical analysis available in the literatures.

Wireless Sensor Networks (WSNs) is an emerging technology holding a great potential for distributed manufacturing automation. The WSN is not only a replacement of the conventional wired sensors, but is a technology that enhances flexible, distributed sensing, and collaborative information processing, which are vital elements for the distributed intelligent manufacturing control paradigm. Currently, the IEC 61499 function blocks have become an industry standard in distributed control for manufacturing automation. This standard has been empowered with the Multi-Agent Systems in a framework for added intelligence in manufacturing automation. In this paper, we explore the integration of the emerging WSN technology into this framework in order to enhance the flexibility and re-configurability through the wireless sensing and distributed information processing provided by a network of wireless sensors. We propose a three tiered model for integrating the WSN into the function blocks for realization of the distributed intelligent manufacturing control system.