Improving devices communication in Industry 4.0 wireless networks

Kunst, Rafael; Avila, Leandro; Binotto, Alécio Pedro Delazari; Pignaton, Edison; Bampi, Sérgio; Rochol, Juergen

doi:10.1016/j.engappai.2019.04.014

Cited by 38 publications

(22 citation statements)

References 21 publications

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“…In terms of communication, the study in [ 17 ] exposed seamless resources sharing architecture involving IoT methods, such as cloud data transference and wireless network approaches, with the intent to improve performance. The authors also simulated scenarios with Matlab software to observe three communication technologies (5G, 4G, and IEEE 802.11), considering different QoS requirements to monitor the proposed solution’s behavior.…”

Section: Related Workmentioning

confidence: 99%

I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned

Silveira

Viana

Lima

et al. 2020

Sensors

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Industry 4.0 considers the combinations of Internet of Things, computing and communication infrastructure, sensors, and artificial intelligence (AI) to provide predictive maintenance and process optimization. These benefits are very relevant to the semiconductor industry, where high reliability and low operating costs are critical for a business’ success. Analyzing the state-of-the-art of the projects that present the implementation of the fourth industrial revolution in semiconductor companies, we noticed mainly two branches of initiatives: (i) articles that explain the final achievements, not detailing how they were assembled and structured; (ii) articles that detail a part of the industry ecosystem, for example, approaching only the communication system or AI algorithms. In this context, this article proposes an I4.0 Pilot as a compilation of lessons learned during an end-to-end development of a reference design applied to a semiconductor packaging and test company. We explore the requirements of clean rooms and information related to sensors and data acquisition boards, in addition to performance details and configurations pertaining to visualization tools and warning notifications. The main contribution appears in presenting the advantages of adopting flexible decisions in the pilot to enable the best characteristics for a final expandable solution. Our final idea is to emphasize the importance of having a pilot project without significant expenses, presenting the reader with the acquired knowledge, and how they can benefit from it.

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Section: Related Workmentioning

confidence: 99%

I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned

Silveira

Viana

Lima

et al. 2020

Sensors

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“…Figure 1 shows the three main functions of CPI. CPI has 31 main characteristics viz: (1) human in the loop, (2) feedback systems, (3) intelligent systems, (4) tight integration, (5) communication networks, (6) networked control, (7) wireless sensing and actuation, (8) adaptif and predictive, (9) non-functional requirements, (10) high safety requirements, (11) dependability, (12) distributed structure, (13) diterminism, (14) real-time and time-awareness, (15) interoperabilty, (16) interfacing with legacy systems, (17) modularity and composability, (18) cyber security, (19) malicious attacks, (20) resilience, (21) privacy, (22) confidentialy, (23) intrusion detection, (24) heterogeneity, (25) multiform time, (26) model of computation, (27) continuous and discrete, (28) concurrency, (29) scalability and complexity management, (30) validation and verification, (31) simulation [25]. CPI block diagram can be illustrated as shown in Figure 2 below.…”

Section: A Cyber Physical Infrastructurementioning

confidence: 99%

Design Training Kits CPI for Vocational Learning in Industry 4.0

Sudira*¹,

Juwanto²

2019

IJRTE

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Cyber Physical Infrastructure (CPI) is one of eight supporting technology infrastructures of Industry 4.0. CPI is built from several technologies including RFID, RTLS, sensors, wireless sensors, actuators, processors, controllers, and data communication network systems. This paper presents the design of vocational learning training kits for CPI competencies in industry 4.0. CPI training kits for developing industry competency 4.0 are designed using the waterfall life-cycle method. Design analysis, design systems, part production, assembling processing, firmware production, systems evaluation, and test used interactively to produce the final product. Industrial 4.0 CPI training kits design includes: (1) PLC processor systems, mini PC, Arduino mega2560; (2) a timer counter controller with a PID systems; (3) RFID and RTLS identifiers; (4) proximity sensor, magnetic sensor, inductive sensor, capacitive sensor, photoelectric sensor, humadity sensor, thermocouple, thermostat, thermistor, opto coupler, limit switch, push button, analog V, ultrasonic, pressure sensor, speed sensor; (5) AC motor actuators, servo motors, stepper motors, valves, pneumatic-hydraulic cylinders, lamps, heaters, relays, SSR, 24V-5V converters; (6) LCD, LED monitor display; (7) data communication between controllers, (8) data communication modules to the internet; (9) user manual and job sheet. The test results show: content experts have given a score of 76.7 from a scale of 100, media experts have given a score of 88.6 from a scale of 100 and users have given a score of 85.4 from a scale of 100. Field trials through training show results can improve competencies in industry 4.0. CPI training kits strongly support the education and training needs of various competencies to develop the capacity of workers in industry 4.0. CPI training kits can be used in schools, universities, and training centers for industrial technology 4.0

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“…Mass data will be generated from the data centre of Industrial 6G applications. It is difficult to sort and summarize these data [4]. However, the valuable information contained in the massive data is a prerequisite for realizing the automated operation of industrial equipment and the system to obtain higher production and communication efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

“…Data mining can dig out potentially valuable information from data [6]. It is an evaluation standard to measure the intelligence level of 6G and the basis for the development of related intelligent applications [7]. Data mining technologies mainly include classification, prediction, and cluster analysis.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

Mukherjee

Goswami

Khan

et al. 2021

IEEE Internet Things J.

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The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). The Industrial equipment in 6G contains a large number of wireless sensors, which collect a large amount of data by sensing the surrounding environment, but the data is not always useful. The emergence of data mining has undoubtedly found a breakthrough point for extracting effective information from massive data. In the pursuit of lower latency, edge computing has also begun to develop. Eventually, 6G can make intelligent decisions in real-time and realize automated equipment operations. However, with the application of various technologies, the energy consumption of the system has increased, but the energy carried by the sensor is still limited. This paper addresses the energy consumption problem with a system model of industrial wireless sensor networks based on a multi-agent system (MAS) for industrial 6G applications. The method uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, we analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

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Improving devices communication in Industry 4.0 wireless networks

Cited by 38 publications

References 21 publications

I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned

I4.0 Pilot Project on a Semiconductor Industry: Implementation and Lessons Learned

Design Training Kits CPI for Vocational Learning in Industry 4.0

Energy-Efficient Resource Allocation Strategy in Massive IoT for Industrial 6G Applications

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