Abstract

The industrial internet of things (IIoT) is related to the fourth industrial revolution and includes different applications and innovations for the conduction of industrial activities. The introduction of IIoT has revolutionized how manufacturing and industrial units traditionally worked. The current paper aims to analyse the privacy and security issues incorporated in the IIoT and the role of the machine learning (ML) approach for the IIoT. It also analyses industry focused internet of things (IoT) taxonomies and the application of IIoT systems in various smart cities. It is found that IIoT-enabled devices are based on advanced technologies such as big data, robotics, ML, operational technology (OT), and machine-to-machine (M2M) technologies. It helps to intelligently change the behaviour of different environments without any human intervention. ML-based IIoT devices can be used for smart energy, smart transportation, urban planning, and smart city characteristics.

Keyword

Industrial internet of things (IIoT), Machine learning (ML), Smart energy, Smart transportation, Sensors.

Cite this article

Cite this Article

Refference

[1]Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

[2]Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

[3]Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

[4]Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

[5]Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

[6]Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

[7]Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

[8]Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

[9]Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

[10]Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

[11]Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.

[12]Obi Y, Claudio KS, Budiman VM, Achmad S, Kurniawan A. Sign language recognition system for communicating to people with disabilities. Procedia Computer Science. 2023; 216:13-20.

[13]Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

[14]Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

[15]Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

[16]Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

[17]Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

[18]Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

[19]Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

[20]Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

[21]Carney M, Webster B, Alvarado I, Phillips K, Howell N, Griffith J, et al. Teachable machine: approachable web-based tool for exploring machine learning classification. In extended abstracts of the 2020 CHI conference on human factors in computing systems 2020 (pp. 1-8). ACM.

[22]Dogo EM, Afolabi OJ, Nwulu NI, Twala B, Aigbavboa CO. A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks. In international conference on computational techniques, electronics and mechanical systems (CTEMS) 2018 (pp. 92-9). IEEE.