SMILE: Smart Monitoring IoT Learning Ecosystem

Roberta Avanzato, Francesco Beritelli, Francesco Di Franco, Michele Russo


In industrial contexts to date, there are several solutions to monitor and intervene in case of anomalies and/or failures. Using a classic approach to cover all the requirements needed in the industrial field, different solutions should be implemented for different monitoring platforms, covering the required end-to-end. The classic cause-effect association process in the field of industrial monitoring requires thorough understanding of the monitored ecosystem and the main characteristics triggering the detected anomalies. In these cases, complex decision-making systems are in place often providing poor results. This paper introduces a new approach based on an innovative industrial monitoring platform, which has been denominated SMILE. It allows offering an automatic service of global modern industry performance monitoring, giving the possibility to create, by setting goals, its own machine/deep learning models through a web dashboard from which one can view the collected data and the produced results.  Thanks to an unsupervised approach the SMILE platform can understand which the linear and non-linear correlations are representing the overall state of the system to predict and, therefore, report abnormal behavior.


unsupervised machine learning; industry 4.0; smart monitoring; internet of things; maintenance perspective.

Full Text:



B. Chen, J. Wan, L. Shu, P. Li, M. Mukherjee, and B. Yin, “Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges,” IEEE ACCESS - Key Technologies for Smart Factory of Industry 4.0, vol. 6, pp. 6505 – 6519, Dec. 2017. DOI: 10.1109/ACCESS.2017.2783682.

H. Xu, W. Yu, D. Griffith, and N. Golmie, “A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective,” IEEE ACCESS - Towards Service-Centric Internet of Things (IoT): From Modeling to Practice, vol. 6, pp. 78238 – 78259, Dec. 2018. DOI: 10.1109/ACCESS.2018.2884906.

Alexei Vladishev, Open Source Enterprise Monitoring with Zabbix, [Online]. Available:

D. C. Huang, C. F. Lin, C. Y. Chen, J. R. Sze, “The Internet Technology for Defect Detection System with Deep Learning Method

in Smart Factory,” 4th International Conference on Information Management (ICIM), Oxford, UK, 25-27 May 2018, Published: June 2018. DOI: 10.1109/INFOMAN.2018.8392817.

R. Ozdemir, M. Koc, “A Quality Control Application on a Smart Factory Prototype Using Deep Learning Methods,” IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 17-20 Sept. 2019, Published: Dec 2019. DOI: 10.1109/STC-CSIT.2019.8929734.

K. Al-Gumaei, A. Müller, J. N. Weskamp, Claudio Santo Longo, F. Pethig, and S. Windmann, “Scalable Analytics Platform for Machine Learning in Smart Production Systems,” 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, 10-13 Sept. 2019, Published: Oct. 2019. DOI: 10.1109/ETFA.2019.8869075.

S. Tamy, H. Belhadaoui, M. A. Rabbah, N. Rabbah, and M. Rifi, “An Evaluation of Machine Learning Algorithms to Detect Attacks in Scada Network,” 7th Mediterranean Congress of Telecommunications (CMT), Fès, Morocco,24-25 Oct. 2019, Published: December 2019. DOI: 10.1109/CMT.2019.8931327.

Rosilah Hassan, Husna Inani Abdul Jabar, Mohammad Khatim Hasan, Meng Chun Lam, Wan Mohd Hirwani Wan Hussain, “Cloud Based Performance Data Analysis and Monitoring System for Express Bus in Malaysia” In International Journal on Advanced Science, Engineering and Information Technology, pages: 1959-1967 DOI:10.18517/ijaseit.9.6.7075

Q. Zhang, L. T. Yang, Z. Yan, Z. Chen, and P. Li, “An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics,”, IEEE Transactions on Industrial Informatics, vol. 14, no 7, pp. 3170 – 3178, July 2018. DOI: 10.1109/TII.2018.2808910.

M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039 – 3071, July 2019. DOI: 10.1109/COMST.2019.2926625.

R. Zhao, R. Yan, J. Wang, and K. Mao, “Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks,” Sensors, vol. 17, no 2, pp. 1-18, Jan. 2017. DOI:

C. L. Philip Chen, C Y. Zhang, L. Chen, and M. Gan, “Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2163 – 2173, Dec. 2015. DOI: 10.1109/TFUZZ.2015.2406889.

Y. Li, and M. Chen, “Software-Defined Network Function Virtualization: A Survey,” IEEE Access - Ultra-Dense Cellular Networks, vol. 3, pp. 2542 – 2553, Dec. 2015. DOI: 10.1109/ACCESS.2015.2499271.

LoRaWAN across the globe: LoRa Internet of Things networks overview, [online] Available:

F. Beritelli, G. Capizzi, G. Lo Sciuto, C. Napoli, and F. Scaglione, “Rainfall Estimation Based on the Intensity of the Received Signal in a LTE/4G Mobile Terminal by Using a Probabilistic Neural Network,” IEEE Access, vol. 6, pp. 30865-30873, May 2018.

R. Avanzato, F. Beritelli, F. Di Franco, and V. Puglisi “A Convolutional Neural Networks approach to Audio Classification for Rainfall Estimation,” In Proc. The 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 18-21 September 2019, Metz (France).

R. Avanzato, and F. Beritelli, “A Rainfall Classification Technique based on the Acoustic Timbre of Rain and Convolutional Neural Networks,” Submitted to: MDPI Sensors.

F. Beritelli, G. Capizzi, G. Lo Sciuto, F. Scaglione, D. Połap, M. Woźniak, “A neural network pattern recognition approach to automatic rainfall classification by using signal strength in LTE/4G networks” In International Joint Conference on Rough Sets, 3 July 2017, pp. 505-512.

Soo See Chai, Wei Keat Wong, Kok Luong Goh, Hui Hui Wang, Yin Chai Wang “Radial Basis Function (RBF) Neural Network: Effect of Hidden Neuron Number, Training Data Size, and Input Variables on Rainfall Intensity Forecasting” In International Journal on Advanced Science, Engineering and Information Technology, Vol. 9 (2019) No. 6, pages: 1921-1926, DOI:10.18517/ijaseit.9.6.10239

F. Beritelli, and A. Spadaccini, “A Statistical Approach to Biometric Identity Verification based on Heart Sounds,” In: Proceedings of the Fourth International Conference on Emerging Security Information, Systems and Technologies, Venice (Italy), 18-25 July 2010, p. 93-96, ISBN: 978-0-7695-4095-5.

F Beritelli, G Capizzi, GL Sciuto, C Napoli, and M Woźniak “A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis,” Neural Networks, vol. 108, pp. 331-338, Dec. 2018.

A. Spadaccini, F. Beritelli, “Performance evaluation of heart sounds biometric systems on an open dataset,” In: IEEE. 2013 18th International Conference on Digital Signal Processing (DSP), pp. 1-5, Fira, Santorini, Greece, 1-3 July 2013.

S. K. Datta, and C. Bonnet, “MEC and IoT Based Automatic Agent Reconfiguration in Industry 4.0,” IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Indore, India, 16-19 Dec. 2018, Published: May 2019. DOI: 10.1109/ANTS.2018.8710126.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development