The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.
Significance of sensors for industry 4.0: Roles, capabilities, and applications
Open accessAbstract
Sensors play a crucial role in factory automation in making the system intellectual. Different types of sensors are available as per the suitability and applications; some of them are produced in mass and available in the market at affordable costs. The standard sensor types available are position sensors, pressure sensors, flow sensors, temperature sensors, and force sensors. They are used in many sectors, such as motorsport, medical, industry, aerospace, agriculture, and daily life. The objective of Industry 4.0 is to increase efficiency through automation. Sensors are vital components of Industry 4.0, allowing several transitions such as changes in positions, length, height, external and dislocations in industrial production facilities to be detected, measured, analysed, and processed. Smart factories will also enhance sustainability by tracking real-time output, and automated control systems will minimise potential factory maintenance costs. It can also be seen that digitalisation can improve production mobility, which gives advanced manufacturing firms a competitive advantage. This paper discusses sensors and their various types, along with significant capabilities for manufacturing. The step-by-step working Blocks and Quality Services of Sensors during implementation in Industry 4.0 are elaborated diagrammatically. Finally, we identified thirteen significant applications of sensors for Industry 4.0. Industry 4.0 provides an excellent opportunity for the development of the sensor market across the globe. In Industry 4.0, sensors will enjoy higher acceptance rates and benefit from a fully enabled connecting and data exchange and logistics integration. In the coming years, sensor installations may grow in process management, automated production lines, and digital supply chains.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems
Lee, J., Bagheri, B., & Kao, H.-A. (2015) · Manufacturing Letters · doi:10.1016/j.mfglet.2014.12.001 · Cited by 5,113
Metal Oxide Gas Sensors: Sensitivity and Influencing Factors
Wang, C., Yin, L., Zhang, L., Xiang, D., & Gao, R. (2010) · Sensors · doi:10.3390/s100302088 · Cited by 2,928
Intelligent Manufacturing in the Context of Industry 4.0: A Review
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017) · Engineering · doi:10.1016/j.eng.2017.05.015 · Cited by 2,913
The expected contribution of Industry 4.0 technologies for industrial performance
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.08.019 · Cited by 2,201
Ivanov, D., Dolgui, A., & Sokolov, B. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1488086 · Cited by 2,055
Humidity Sensors: A Review of Materials and Mechanisms
Chen, Z., & Lu, C. (2005) · Sensor Letters · doi:10.1166/sl.2005.045 · Cited by 1,501
Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018) · Process Safety and Environmental Protection · doi:10.1016/j.psep.2018.05.009 · Cited by 1,440
Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016) · Computer Networks · doi:10.1016/j.comnet.2015.12.017 · Cited by 1,404
Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review
Farahani, H., Wagiran, R., & Hamidon, M. (2014) · Sensors · doi:10.3390/s140507881 · Cited by 1,286
The industrial management of SMEs in the era of Industry 4.0
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018) · International Journal of Production Research · doi:10.1080/00207543.2017.1372647 · Cited by 1,188
Research works printed but not linked: 37
- Schütze, 2018, Sensors 4.0–smart sensors and measurement technology enable Industry 4.0, Journal of Sensors and Sensor Systems, 7, 359, 10.5194/jsss-7-359-2018 · doi:10.5194/jsss-7-359-2018
- Yoon, 2021, Highly stretchable metal-polymer hybrid conductors for wearable and self-cleaning sensors, NPG Asia Mater., 13, 1, 10.1038/s41427-020-00277-6 · doi:10.1038/s41427-020-00277-6
- Wang, 2021, Application of three self-developed ECT sensors for monitoring the moisture content in sand and mortar, Construct. Build. Mater., 267, 121008, 10.1016/j.conbuildmat.2020.121008 · doi:10.1016/j.conbuildmat.2020.121008
- Farahani, 2019, Evaluation of in-mold sensors and machine data towards enhancing product quality and process monitoring via Industry 4.0, Int. J. Adv. Manuf. Technol., 105, 1371, 10.1007/s00170-019-04323-8 · doi:10.1007/s00170-019-04323-8
- Grantz, 2021, Age-specific social mixing of school-aged children in a US setting using proximity detecting sensors and contact surveys, Sci. Rep., 11, 1, 10.1038/s41598-021-81673-y · doi:10.1038/s41598-021-81673-y
- Delipinar, 2021, Fabrication and materials integration of flexible humidity sensors for emerging applications, ACS Omega, 6, 8744, 10.1021/acsomega.0c06106 · doi:10.1021/acsomega.0c06106
- Lebosse, 2011, Modeling and evaluation of low-cost force sensors, IEEE Trans. Robot., 27, 815, 10.1109/TRO.2011.2119850 · doi:10.1109/tro.2011.2119850
- Vecchi, 2000, Experimental evaluation of two commercial force sensors for applications in biomechanics and motor control, 5th Ann. Conf. of Int. FES
- Ejeian, 2019, Design and applications of MEMS flow sensors: a review, Sens. Actuators, A, 295, 483, 10.1016/j.sna.2019.06.020 · doi:10.1016/j.sna.2019.06.020
- Repelianto A, 2019, The improvement of flaw detection by the configuration of uniform eddy current probes, Sensors, 19, 397, 10.3390/s19020397 · doi:10.3390/s19020397
- Sun, 2005, Enhanced flaw detection using an eddy current probe with a linear array of hall sensors, vol. 760, 516
- Gómez-Robledo, 2013, Using the mobile phone as Munsell soil-colour sensor: an experiment under controlled illumination conditions, Comput. Electron. Agric., 99, 200, 10.1016/j.compag.2013.10.002 · doi:10.1016/j.compag.2013.10.002
- Othman, 2014, Fuzzy ripening mango index using RGB colour sensor model, Researchers World, 5, 1
- Sakai, 2021, In-vacuum active colour sensor and wireless communication across a vacuum-air interface, Sci. Rep., 11, 1, 10.1038/s41598-020-80501-z · doi:10.1038/s41598-020-80501-z
- Shi, 2021, A simultaneous calibration technique of the extrinsic and turntable for structured-light-sensor-integrated CNC system, Opt Laser. Eng., 138, 106451, 10.1016/j.optlaseng.2020.106451 · doi:10.1016/j.optlaseng.2020.106451
- Minaee, 2021, Deep-emotion: facial expression recognition using attentional convolutional network, Sensors, 21, 3046, 10.3390/s21093046 · DOI
- Elashery, 2021, Novel and selective potentiometric sensors for Cinchocaine HCl determination in its pure and Co-formulated dosage form: a comparative study of in situ carbon sensors based on different ion pairing agents, Measurement, 173, 108549, 10.1016/j.measurement.2020.108549 · doi:10.1016/j.measurement.2020.108549
- Mueller, 2018, Wireless sensors for industry 4.0-wireless communication and wireless powering, Smart SysTech
- Sigg, 2019, Capturing human-machine interaction events from radio sensors in industry 4.0 environments, International Conference on Business Process Management, 430
- Mishra, 2020, Real-time monitoring and control of friction stir welding process using multiple sensors, CIRP Journal of Manufacturing Science and Technology, 30, 1, 10.1016/j.cirpj.2020.03.004 · doi:10.1016/j.cirpj.2020.03.004
- Braunmüller, 2020, Advanced sensors: industry 4.0 for the laser microjet technology, vol. 11268, 112681M
- Pinto, 2021, Antibiofouling strategy for optical sensors by chlorine generation using low-cost, transparent and highly efficient electrodes based on platinum nanoparticles coated oxide, Chem. Eng. J., 404, 126479, 10.1016/j.cej.2020.126479 · doi:10.1016/j.cej.2020.126479
- Benitez R, Ramirez C, Vazquez JA. Sensors Calibration for Metrology 4.0. 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4. 0&IoT) 2019 Jun 4 (pp. 296-299). IEEE. · doi:10.1109/metroi4.2019.8792886
- Abarca, 2009, Intelligent sensor for tracking and monitoring of blood temperature and hemoderivatives used for transfusions, Sens. Actuators, A, 152, 241, 10.1016/j.sna.2009.03.018 · doi:10.1016/j.sna.2009.03.018
- Simon, 2001, Micromachined metal oxide gas sensors: opportunities to improve sensor performance, Sensor. Actuator. B Chem., 73, 1, 10.1016/S0925-4005(00)00639-0 · doi:10.1016/s0925-4005(00)00639-0
- Quinn, 2010, Use of environmental sensors and sensor networks to develop water and salinity budgets for seasonal wetland real-time water quality management, Environ. Model. Software, 25, 1045, 10.1016/j.envsoft.2009.10.011 · doi:10.1016/j.envsoft.2009.10.011
- Gaynor, 2008, Implications of sensors and sensor networks for data quality management, Int. J. Inf. Qual., 2, 75
- Rada, 2016, Perspectives of low-cost sensors adoption for air quality monitoring, InAir Quality, 29
- Entler, 2021, Ceramic-chromium hall sensors for environments with high temperatures and neutron radiation, Sensors, 21, 721, 10.3390/s21030721 · doi:10.3390/s21030721
- Indri, 2019, Smart sensors applications for a new paradigm of a production line, Sensors, 19, 650, 10.3390/s19030650 · doi:10.3390/s19030650
- Dhinesh, 2020, A review on 3D printed sensors, vol. 764
- Qian, 2021, Facile preparation of self-assembled black phosphorus-based composite LB films as new chemical gas sensors, Colloid. Surface. Physicochem. Eng. Aspect., 608, 125616, 10.1016/j.colsurfa.2020.125616 · doi:10.1016/j.colsurfa.2020.125616
- Nica, 2020, Smart connected sensors, industrial big data, and real-time process monitoring in cyber-physical system-based manufacturing, J. Self Govern. Manag. Econ., 8, 29, 10.22381/JSME8420203 · doi:10.22381/jsme8420203
- Cui, 2021, Stretchable strain sensors with dentate groove structure for enhanced sensing recoverability, Compos. B Eng., 211, 108641, 10.1016/j.compositesb.2021.108641 · doi:10.1016/j.compositesb.2021.108641
- Janak, 2016, Quantities and sensors for machine tool spindle condition monitoring, MM science journal, 2016, 1648, 10.17973/MMSJ.2016_12_2016204 · doi:10.17973/mmsj.2016_12_2016204
- Mahato, 2020, Clinically comparable impedimetric immunosensor for serum alkaline phosphatase detection based on electrochemically engineered Au-nano-Dendroids and graphene oxide nanocomposite, Biosens. Bioelectron., 148, 111815, 10.1016/j.bios.2019.111815 · doi:10.1016/j.bios.2019.111815
- Dutta, 2020, Nanobiosensor-based diagnostic system: transducers and surface materials, Nano biomaterials Engineering, 1
Cited by
Mbunge, E., Muchemwa, B., Jiyane, S., & Batani, J. (2021) · Global Health Journal · doi:10.1016/j.glohj.2021.11.008 · Cited by 348
Sensors and Techniques for On-Line Determination of Cell Viability in Bioprocess Monitoring
Rösner, L., Walter, F., Ude, C., John, G., & Beutel, S. (2022) · Bioengineering · doi:10.3390/bioengineering9120762 · Cited by 42
Metadata
- Authors
- Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R.
- Venue
- Sensors International
- Publisher
- Elsevier BV
- Published
- 2021
- Volume
- 2
- Pages
- 100110
- ISSN
- 2666-3511 (ISSN-L)
- OpenAlex
- W3173907290
- Cited by
- 355
- References
- 125
- FWCI
- 29.17
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- creativecommons.org/licenses/by/4.0/ (vor, 166 d)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Significance of sensors for industry 4.0: Roles, capabilities, and applications. https://app.cochat.ai/papers/10.1016/j.sintl.2021.100110
- The paper
- Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021). Significance of sensors for industry 4.0: Roles, capabilities, and applications. Sensors International. https://doi.org/10.1016/j.sintl.2021.100110
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.