IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE AND IOT TECHNOLOGIES IN INTELLIGENT MEASURING SENSOR SYSTEMS FOR VEGETABLE OIL PRODUCTION
Keywords:
artificial intelligence, Internet of Things, intelligent sensors, industrial IoT, vegetable oil production, Edge AI, TinyML.Abstract
The article presents a systematic overview of modern approaches to integrating artificial intelligence (AI) and the Internet of Things (IoT) technologies into intelligent measuring sensor systems, with a focused application in vegetable oil production. Architectural models, data processing methods, machine learning algorithms, energy efficiency, security, and scalability issues are examined in detail. Applications in industry, energy, transport, medicine, and smart city systems are analyzed for broader context, while special attention is devoted to the unique requirements of vegetable oil processing stages, including raw material reception and quality assessment, seed cleaning and conditioning, mechanical pressing or solvent extraction, refining (degumming, neutralization, bleaching, deodorization), quality control (acidity, peroxide value, color, clarity, oxidative stability), and final storage and packaging. Particular emphasis is placed on the Edge AI and TinyML concepts, which ensure the transfer of intelligent processing directly to the sensor level, enabling real-time decision-making with minimal latency. The prospects for the development of neuromorphic computing, self-sustaining sensory nodes, and seamless integration with 5G/6G infrastructure are thoroughly substantiated, highlighting their potential to enhance sustainability, product quality, and operational efficiency in the vegetable oil industry.
References
Hinton G., LeCun Y., & Bengio Y. (2015). Deep learning. Nature, 521, 436–444. https://doi.org/10.1038/nature14539
L. Atzori, A. Iera, G. Morabito. “The Internet of Things: A Survey” // Computer Networks, vol. 54, no. 15, pp. 2787–2805, 2010. DOI: 10.1016/j.comnet.2010.05.010
A. Zanella et al. “Internet of Things for Smart Cities” // IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, 2014. DOI: 10.1109/JIOT.2014.2306328
H. Ning, Z. Wang. “Future Internet of Things Architecture” // IEEE Communications Magazine, vol. 49, no. 11, pp. 104–110, 2011. DOI: 10.1109/MCOM.2011.6069719
D. Evans. “The Internet of Things: How the Next Evolution of the Internet Is Changing Everything” // Cisco White Paper, 2011.
K. Ashton. “That ‘Internet of Things’ Thing” RFID Journal, 2009.
X. Fang et al. “Smart Grid – The New and Improved Power Grid” // IEEE Communications Surveys & Tutorials, 2012. DOI: 10.1109/SURV.2011.101911.00087
A. Ghasempour, “Internet of Things in Smart Grid,” IEEE Internet of Things Journal, 2019. DOI: 10.1109/JIOT.2018.2872777
J. Fraden, Handbook of Modern Sensors, Springer, 2016.
R. Isermann, “Model-Based Fault Detection,” Control Engineering Practice, 2005. DOI: 10.1016/j.conengprac.2004.12.005
A. Mahmood et al., “Reliability in IoT,” IEEE Access, 2018.
DOI: 10.1109/ACCESS.2018.2834119
S. Li et al., “Industrial Internet,” IEEE Transactions on Industrial Informatics, 2017. DOI: 10.1109/TII.2017.2786779
H. Boyes et al., “Industrial IoT Security Framework,” Computer Law & Security Review, 2018. DOI: 10.1016/j.clsr.2017.12.005
Y. LeCun, Y. Bengio, G. Hinton. “Deep Learning” // Nature, vol. 521, pp. 436–444, 2015. DOI: 10.1038/nature14539
G. Hinton et al. “Deep Neural Networks for Acoustic Modeling” // IEEE Signal Processing Magazine, vol. 29, no. 6, 2012. DOI: 10.1109/MSP.2012.2205597
F. Adelantado et al. “Understanding the Limits of LoRaWAN” // IEEE Communications Magazine, 2017. DOI: 10.1109/MCOM.2017.1600613
J. G. Andrews et al. “What Will 5G Be?” // IEEE JSAC, 2014.
DOI: 10.1109/JSAC.2014.2328098
M. Zorzi et al. “From Today’s INTRAnet to the Internet of Things” // IEEE Wireless Communications, 2010. DOI: 10.1109/MWC.2010.5675772
Y. Mao et al. “Mobile Edge Computing: Survey” // IEEE Communications Surveys & Tutorials, 2017. DOI: 10.1109/COMST.2017.2682318
S. Deng et al. “Edge Intelligence: The Confluence of Edge Computing and AI” // IEEE Internet of Things Journal, 2020. DOI: 10.1109/JIOT.2020.2984887
N. D. Lane et al. “DeepX: A Software Accelerator for Low-Power Deep Learning” // IPSN, 2016. DOI: 10.1145/2883817.2883822
Q. Yang et al. “Federated Machine Learning” // ACM TIST, 2019.
DOI: 10.1145/3298981
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