Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis Análisis de los datos

Main Article Content

Sivadi Balakrishna
Moorthy Thirumaran
Vijender Kumar Solanki

Abstract

Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019.


Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data.


Objective:  To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data. 


Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations.


Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures.


Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes.


Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads.


Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
BalakrishnaS., ThirumaranM., and SolankiV., “Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis: Análisis de los datos”, ing. Solidar, vol. 16, no. 1, Jan. 2020.
Section
Research Articles

References

[1] S. Balakrishna and M. Thirumaran, “Semantic Interoperable Traffic Management Framework for IoT Smart City Applications,” EAI Endorsed Transactions on Internet of Things, vol. 4, no. 13 pp. 1-17, 2018. [Online]. doi: 10.4108/eai.11-9-2018.15548

[2] S. Balakrishna and M. Thirumaran, “Towards an Optimized Semantic Interoperability Framework for IoT-Based Smart Home Applications,” in Internet of Things and Big Data Analytics for Smart Generation. Balas V., Solanki V., Kumar R., Khari M. (Eds). Intelligent Systems Reference Library, vol 154. Springer, Cham, pp 185-211, 2019.

[3] S. Balakrishna and M. Thirumaran, “Programming Paradigms for IoT Applications: An Exploratory Study”, in Handbook of IoT and Big Data. Solanki, V., Díaz, V., Davim, J. (Eds.). Boca Raton: CRC press, Taylor & Francis Group, pp 23-57, 2019.

[4] S. Balakrishna, V. Kumar Solanki, V. Kumar Gunjan and M. Thirumaran, “Performance Analysis of Linked Stream Big Data Processing Mechanisms for Unifying IoT Smart Data” International Conference on Intelligent Computing and Communication Technologies (ICICCT), Springer, pp. 680-688, 2019.

[5] S. Bhadra, A. Kundu and S. K. Guha, “An Agent based Efficient Traffic Framework using Fuzzy”, Fourth International Conference on Advanced Computing & Communication Technologies, pp. 57, 2014.

[6] A. Mallik, H. Ghosh, S. Chaudhury and G. Harit, “MOWL: An Ontology Representation Language for Web-based Multimedia Applications”, ACM Trans. Multimedia Comput. Commun. Appl., vol. 10, no. 1, pp. 21, 2013.

[7] P. Pyykonen, J. Laitinen, J. Viitanen, P. Eloranta and Korhonen, “IoT for Intelligent Traffic System, IoT for intelligent traffic system”, International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, p. 10, 2013.

[8] Q. Zhang, T. Huang, Y. Zhu, and M. Qiu, “A case study of sensor data collection and analysis in smart city: provenance in smart food supply chain,” International Journal of Distributed Sensor Networks, vol. 9, no. 11, pp 382-132, 2013.

[9] K. Kotis and A. Katasonov, “Semantic Interoperability on the Web of Things: The Smart Gateway Framework”, Proceedings of the Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS-2012). Palermo, pp 25-35, 2012.

[10] D. Bandyopadhyay and J. Sen, “The internet of things - applications and challenges in technology and Standardization,” Springer International Journal of Wireless Personal Communications, vol. 58, no. 1, pp. 49-69, 2011.

[11] D. Singh, G. Tripathi and A. J. Jara, “A survey of Internet-of-Things: Future Vision, Architecture, Challenges and Services,” IEEE World of Forum on Internet of Things, pp. 15-20, 2017.

[12] P. Pyykonen, J. Laitinen, J. Viitanen, P. Eloranta and Korhonen, “IoT for Intelligent Traffic System, IoT for intelligent traffic system,” International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, pp. 251-257, 2013.

[13] X. Yu, F. Sun and X. Cheng, “Intelligent Urban Traffic Management System Based on Cloud Computing and Internet of Things,” International Conference on Computer Science & Service System, IEEE, pp. 216, 2012.

[14] M. Mazhar Rathore, P. Anand, A. Awais and R. Suengmin , “Urban planning and building smart cities based on the internet of things using big data analytics,” Computer Networks, Elsevier, pp. 1-22, 2016. [Online]. doi: 10.1016/j.comnet.2015.12.023

[15] A.P. Plageras, K.E. Psannis, C. Stergiou, H. Wang and B.B. Gupta, Efficient IoT-based sensor BIG Data collection-processing and analysis in Smart Buildings, Future Generation Computer Systems, Elsevier, pp 1-15, 2017. [Online]. https://doi.org/10.1016/j.future.2017.09.082

[16] Q. Liu Qiang Liu, Y. Ma, M. Alhussein, L. Peng and Y. Zhang, “Green data center with IoT sensing and cloud-assisted smart temperature controlling system,” Computer Networks, Elsevier, pp 1-11, 2015. [Online]. http://dx.doi.org/10.1016/j.comnet.2015.11.024

[17] S. Pasha, “Thingspeak Based Sensing and Monitoring System for IoT with Matlab Analysis,” International Journal of New Technology and Research (IJNTR), vol. 2, no. 6, pp. 19-23, 2016.

[18] A. Adnan, G. Kousiouris, H. Pervaiz, J. Sancho, P. Ta-Shma, F. Carrez, and K. Moessner, “Real-time probabilistic data fusion for large-scale IoT applications,” IEEE Access, vol 6, no 4, pp 10015-10027, 2018.

[19] L. Lengyel, P. Ekler, T. Ujj, T. Balogh, and H. Charaf, “SensorHUB: An IoT Driver Framework for Supporting Sensor Networks and Data Analysis,” International Journal of Distributed Sensor Networks, Hindawi, vol 11, no. 3, pp 1-12, 2015.

[20] S. Balakrishna, M. Thirumaran, and V. Kumar Solanki, “A Framework for IoT Sensor Data Acquisition and Analysis,” EAI Endorsed Transactions on Internet of Things, vol 18, no. 1, pp 1-13, 2019. [Online]. http://dx.doi.org/10.4108/eai.21-12-2018.159410