Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jan 22;24(2):712.
doi: 10.3390/s24020712.

IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop

Hicham Klaina  1 , Imanol Picallo  1 , Peio Lopez-Iturri  1   2 , Aitor Biurrun  1 , Ana V Alejos  3 , Leyre Azpilicueta  1   2 , Abián B Socorro-Leránoz  1   2 , Francisco Falcone  1   2   4
Affiliations

IIoT Low-Cost ZigBee-Based WSN Implementation for Enhanced Production Efficiency in a Solar Protection Curtains Manufacturing Workshop

Hicham Klaina et al. Sensors (Basel). .

Abstract

Nowadays, the Industry 4.0 concept and the Industrial Internet of Things (IIoT) are considered essential for the implementation of automated manufacturing processes across various industrial settings. In this regard, wireless sensor networks (WSN) are crucial due to their inherent mobility, easy deployment and maintenance, scalability, and low power consumption, among other benefits. In this context, the presented paper proposes an optimized and low-cost WSN based on ZigBee communication technology for the monitoring of a real manufacturing facility. The company designs and manufactures solar protection curtains and aims to integrate the deployed WSN into the Enterprise Resource Planning (ERP) system in order to optimize their production processes and enhance production efficiency and cost estimation capabilities. To achieve this, radio propagation measurements and 3D ray launching simulations were conducted to characterize the wireless channel behavior and facilitate the development of an optimized WSN system that can operate in the complex industrial environment presented and validated through on-site wireless channel measurements, as well as interference analysis. Then, a low-cost WSN was implemented and deployed to acquire real-time data from different machinery and workstations, which will be integrated into the ERP system. Multiple data streams have been collected and processed from the shop floor of the factory by means of the prototype wireless nodes implemented. This integration will enable the company to optimize its production processes, fabricate products more efficiently, and enhance its cost estimation capabilities. Moreover, the proposed system provides a scalable platform, enabling the integration of new sensors as well as information processing capabilities.

Keywords: 3D ray launching; Industry 4.0; automation; industrial internet of things; manufacturing process; wireless channel; wireless sensor networks.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Different workstations and areas of the Galeo factory workshop.
Figure 2
Figure 2
RF interference measurements within the factory workshop at the 2.4 GHz band.
Figure 3
Figure 3
The created workshop scenario for its simulation by the 3D Ray Launching tool.
Figure 4
Figure 4
The two measurement campaigns carried out for the validations of the 3D ray launching algorithm, (a) measurement points (1 to 16) covering the entire area of the workshop and (b) measurements for a LoS linear path.
Figure 5
Figure 5
Employed transmitter and receiver configuration for the radio channel measurements: (a) the VCO as a transmitter; (b) the spectrum analyzer as a receiver.
Figure 6
Figure 6
Measurements vs. 3D-RL simulation results for (a) scenario 1 and (b) scenario 2.
Figure 7
Figure 7
Estimated power delay profiles at different locations (measurement points) for both TX1 and TX2 (a) location 3; (b) location 10; (c) location 13; (d) location 15.
Figure 8
Figure 8
The diagram shows 2.4 GHz RF power level distribution maps at different heights for scenario 2.
Figure 9
Figure 9
Bidimensional RF power level distribution planes for (a) scenario 1, and (b) scenario 2.
Figure 10
Figure 10
Two-dimensional planes for ZigBee notes’ sensitivity compliance for (a) scenario 1 and (b) scenario 2.
Figure 11
Figure 11
Schematic view of the WSN deployment within the workshop (‘C’ represents the ZigBee network coordinator/gateway).
Figure 12
Figure 12
(a) The implemented sensor nodes and Coordinator/Gateway; (b) Picture of a sensor node; (c) The node encapsulated.
Figure 13
Figure 13
The implemented nodes at different workstations: (a) knife cutting machine; (b) laser cutting machine; (c) thermal welding machine 1; (d) thermal welding machine 2; (e) automatic slat machine.
Figure 14
Figure 14
Participation of employees and workstations in different product fabrications.
Figure 15
Figure 15
Data analytics examples: (a) time that each product manufacturing process consumed during two complete working days; (b) time spent by a product at each workstation; (c) employees that took part in the manufacturing of a product.
Figure 15
Figure 15
Data analytics examples: (a) time that each product manufacturing process consumed during two complete working days; (b) time spent by a product at each workstation; (c) employees that took part in the manufacturing of a product.
Figure 16
Figure 16
Time consumed manufacturing products in the heat-welding workstation, by two different workers: (a) Employee 6; (b) Employee 17.
Figure 16
Figure 16
Time consumed manufacturing products in the heat-welding workstation, by two different workers: (a) Employee 6; (b) Employee 17.
See this image and copyright information in PMC

References

    1. Sisinni E., Saifullah A., Han S., Jennehag U., Gidlund M. Industrial Internet of Things: Challenges, Opportunities, and Directions. IEEE Trans. Ind. Inform. 2018;14:4724–4734. doi: 10.1109/TII.2018.2852491. - DOI
    1. Ahmed A., Valtiner D., Thomos C., Dielacher F. Wireless connectivity in Industrial sensor and control networks: Challenges and issues in a real implementation for a smart production use-case; Proceedings of the 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA); Vienna, Austria. 8–11 September 2020.
    1. Islam K., Shen W., Wang X. Wireless Sensor Network Reliability and Security in Factory Automation: A Survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2012;42:1243–1256. doi: 10.1109/TSMCC.2012.2205680. - DOI
    1. Song Y., Yu F.R., Zhou L., Yang X., He Z. Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey. IEEE Internet Things J. 2021;8:4250–4274. doi: 10.1109/JIOT.2020.3034385. - DOI
    1. Lu X., Kim I.H., Xhafa A., Zhou J. WSN for Machine Area Network applications; Proceedings of the 46th European Solid-State Device Research Conference (ESSDERC); Lausanne, Switzerland. 12–15 September 2016.

LinkOut - more resources