research-article

A Classification Strategy for Internet of Things Data Based on the Class Separability Analysis of Time Series Dynamics

Authors:

João B. Borges,

Heitor S. Ramos,

Antonio A. F. LoureiroAuthors Info & Claims

ACM Transactions on Internet of Things, Volume 3, Issue 3

Article No.: 23, Pages 1 - 30

https://doi.org/10.1145/3533049

Published: 13 July 2022 Publication History

Abstract

This article proposes TSCLAS, a time series classification strategy for the Internet of Things (IoT) data, based on the class separability analysis of their temporal dynamics. Given the large number and incompleteness of IoT data, the use of traditional classification algorithms is not possible. Thus, we claim that solutions for IoT scenarios should avoid using raw data directly, preferring their transformation to a new domain. In the ordinal patterns domain, it is possible to capture the temporal dynamics of raw data to distinguish them. However, to be applied to this challenging scenario, TSCLAS follows a strategy for selecting the best parameters for the ordinal patterns transformation based on maximizing the class separability of the time series dynamics. We show that our method is competitive compared to other classification algorithms from the literature. Furthermore, TSCLAS is scalable concerning the length of time series and robust to the presence of missing data gaps on them. By simulating missing data gaps as long as 50% of the data, our method could beat the accuracy of the compared classification algorithms. Besides, even when losing in accuracy, TSCLAS presents lower computation times for both training and testing phases.

References

[1]

Andre L. L. Aquino, Tamer S. G. Cavalcante, Eliana S. Almeida, Alejandro C. Frery, and Osvaldo A. Rosso. 2015. Characterization of vehicle behavior with information theory. The European Physical Journal B 88, 10 (2015), 257.

[2]

Andre L. L. Aquino, Heitor S. Ramos, Alejandro C. Frery, Leonardo P. Viana, Tamer S. G. Cavalcante, and Osvaldo A. Rosso. 2017. Characterization of electric load with information theory quantifiers. Physica A: Statistical Mechanics and Its Applications 465 (2017), 277–284.

[3]

Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large, and Eamonn Keogh. 2017. The great time series classification bake off: A review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery 31, 3 (2017), 606–660.

Digital Library

[4]

Anthony Bagnall, Jason Lines, Jon Hills, and Aaron Bostrom. 2015. Time-series classification with COTE: The collective of transformation-based ensembles. IEEE Transactions on Knowledge and Data Engineering 27, 9 (2015), 2522–2535.

Digital Library

[5]

Christoph Bandt and Bernd Pompe. 2002. Permutation entropy: A natural complexity measure for time series. Physical Review Letters 88, 17 (2002), 174102.

[6]

Korkut Bekiroglu, Seshadhri Srinivasan, Ethan Png, Rong Su, and Constantino Lagoa. 2020. Recursive approximation of complex behaviours with IoT-data imperfections. IEEE/CAA Journal of Automatica Sinica 7, 3 (2020), 656–667.

[7]

João B. Borges, Heitor S. Ramos, Raquel A. F. Mini, Osvaldo A. Rosso, Alejandro C. Frery, and Antonio A. F. Loureiro. 2019. Learning and distinguishing time series dynamics via ordinal patterns transition graphs. Appl. Math. Comput. 362 (2019), 124554.

Digital Library

[8]

Joao B. Borges, Heitor S. Ramos, Raquel A. F. Mini, Aline C. Viana, and Antonio A. F. Loureiro. 2019. The quest for sense: Physical phenomena Classification in the Internet of Things. In Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 701–708.

[9]

João Borges Neto, Thiago Silva, Renato Assunção, Raquel Mini, and Antonio Loureiro. 2015. Sensing in the collaborative Internet of Things. Sensors 15, 3 (2015), 6607–6632.

[10]

Leo Breiman. 2001. Random forests. Machine Learning 45, 1 (2001), 5–32.

Digital Library

[11]

Thomas Buchholz, Michael Schiffers, Axel Küpper, and Michael Schiffers. 2003. Quality of context: What it is and why we need it. In Proceedings of the Workshop of the HP OpenView University Association(Geneve, Switzerland), 1–14.

[12]

J. Orestes Cerdeira, M. João Martins, and Pedro C. Silva. 2012. A combinatorial approach to assess the separability of clusters. Journal of Classification 29, 1 (2012), 7–22.

Digital Library

[13]

Long Chen, Linqing Wang, Zhongyang Han, Jun Zhao, and Wei Wang. 2019. Variational inference based kernel dynamic Bayesian networks for construction of prediction intervals for industrial time series with incomplete input. IEEE/CAA Journal of Automatica Sinica 7, 5 (2019), 1–9.

[14]

Houtao Deng, George Runger, Eugene Tuv, and Vladimir Martyanov. 2013. A time series forest for classification and feature extraction. Information Sciences 239 (2013), 142–153.

Digital Library

[15]

Bruna Amin Gonçalves, Laura Carpi, Osvaldo A. Rosso, and Martín G. Ravetti. 2016. Time series characterization via horizontal visibility graph and Information Theory. Physica A 464(September2016), 93–102.

[16]

John Greene. 2001. Feature subset selection using Thornton’s separability index and its applicability to a number of sparse proximity-based classifiers. In Proceedings of the Pattern Recognition Association of South Africa.

[17]

Aimad Karkouch, Hajar Mousannif, Hassan Al Moatassime, and Thomas Noel. 2016. Data quality in Internet of Things: A state-of-the-art survey. Journal of Network and Computer Applications 73 (2016), 57–81.

Digital Library

[18]

Christopher W. Kulp, Jeremy M. Chobot, Helena R. Freitas, and Gene D. Sprechini. 2016. Using ordinal partition transition networks to analyze ECG data. Chaos: An Interdisciplinary Journal of Nonlinear Science 26, 7 (2016), 73114.

[19]

Christopher W. Kulp and Suzanne Smith. 2011. Characterization of noisy symbolic time series. Physical Review E 83, 2 (2011), 26201.

[20]

C. W. Kulp and L. Zunino. 2014. Discriminating chaotic and stochastic dynamics through the permutation spectrum test. Chaos: An Interdisciplinary Journal of Nonlinear Science 24, 3 (2014), 33116.

[21]

P. W. Lamberti, M. T. Martin, A. Plastino, and O. A Rosso. 2004. Intensive entropic non-triviality measure. Physica A: Statistical Mechanics and its Applications 334, 1–2 (2004), 119–131.

[22]

H. A. Larrondo, M. T. Martín, C. M. González, A. Plastino, and O. A. Rosso. 2006. Random number generators and causality. Physics Letters A 352, 4–5 (2006), 421–425.

[23]

Jason Lines and Anthony Bagnall. 2015. Time series classification with ensembles of elastic distance measures. Data Mining and Knowledge Discovery 29, 3 (2015), 565–592.

Digital Library

[24]

Jason Lines, Sarah Taylor, and Anthony Bagnall. 2018. Time series classification with HIVE-COTE. ACM Transactions on Knowledge Discovery from Data 12, 5 (2018), 1–35.

[25]

Jason Lines, Sarah Taylor, Anthony Bagnall, and East Anglia. 2016. HIVE-COTE: The hierarchical vote collective of transformation-based ensembles for time series classification. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM), Vol. 12. IEEE, 1041–1046.

[26]

Caihua Liu, Patrick Nitschke, Susan P. Williams, and Didar Zowghi. 2020. Data quality and the Internet of Things. Computing 102, 2 (2020), 573–599.

Digital Library

[27]

Markus Löning, Anthony Bagnall, Sajaysurya Ganesh, Viktor Kazakov, Jason Lines, and Franz J. Király. 2019. SKTIME: A unified interface for machine learning with time series. Workshop on Systems for ML at NeurIPS 2019NeurIPS (2019).

[28]

S. Makhija, S. Saha, S. Basak, and M. Das. 2019. Separating stars from quasars: Machine learning investigation using photometric data. Astronomy and Computing 29 (2019), 100313.

[29]

Kezhi Z. Mao and Wenyin Tang. 2011. Recursive mahalanobis separability measure for gene subset selection. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, 1 (2011), 266–272.

Digital Library

[30]

Samuel Maurus and Claudia Plant. 2016. Skinny-dip: Clustering in a sea of noise. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, 1055–1064.

[31]

Michael McCullough, Michael Small, Thomas Stemler, and Herbert Ho-Ching Iu. 2015. Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science 25, 5 (2015), 53101.

[32]

Mehdi Mohammadi, Ala Al-Fuqaha, Sameh Sorour, and Mohsen Guizani. 2018. Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials 20, 4 (2018), 2923–2960.

Digital Library

[33]

Federico Montori, Kewen Liao, Prem Prakash Jayaraman, Luciano Bononi, Timos Sellis, and Dimitrios Georgakopoulos. 2018. Classification and annotation of open Internet of Things datastreams. In Web Information Systems Engineering – WISE 2018 (Lecture Notes in Computer Science), Hakim Hacid, Wojciech Cellary, Hua Wang, Hye-Young Paik, and Rui Zhou (Eds.). Vol. 1. Springer International Publishing, 209–224.

[34]

F. Olivares, M. Zanin, L. Zunino, and D. G. Pérez. 2020. Contrasting chaotic with stochastic dynamics via ordinal transition networks. Chaos: An Interdisciplinary Journal of Nonlinear Science 30, 6 (2020), 063101.

[35]

Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay. 2011. Scikit-learn: Machine learning in python. Journal of Machine Learning Research 12 (2011), 2825–2830.

Digital Library

[36]

Michael Postol, Candace Diaz, Robert Simon, and Drew Wicke. 2019. Time-series data analysis for classification of noisy and incomplete Internet-of-Things datasets. In Proceedings of the 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 1543–1550.

[37]

Yongrui Qin, Quan Z. Sheng, Nickolas J. G. Falkner, Schahram Dustdar, Hua Wang, and Athanasios V. Vasilakos. 2016. When things matter: A survey on data-centric Internet of Things. Journal of Network and Computer Applications 64 (2016), 137–153.

Digital Library

[38]

Chotirat Ann Ratanamahatana and Eamonn Keogh. 2005. Three myths about dynamic time warping data mining. In Proceedings of the 2005 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, Philadelphia, PA, 506–510.

[39]

Martín Gómez Ravetti, Laura C. Carpi, Bruna Amin Gonçalves, Alejandro C. Frery, and Osvaldo A. Rosso. 2014. Distinguishing noise from chaos: Objective versus subjective criteria using horizontal visibility graph. PLoS ONE 9, 9 (2014), e108004.

[40]

Haroldo V. Ribeiro, Max Jauregui, Luciano Zunino, and Ervin K. Lenzi. 2017. Characterizing time series via complexity-entropy curves. Physical Review E 95, 6 (2017), 062106.

[41]

Osvaldo A. Rosso, Laura C. Carpi, Patricia M. Saco, Martín Gómez Ravetti, Angelo Plastino, and Hilda A. Larrondo. 2012. Causality and the entropy-complexity plane: Robustness and missing ordinal patterns. Physica A: Statistical Mechanics and its Applications 391, 1–2 (2012), 42–55.

[42]

O. A. Rosso, H. A. Larrondo, M. T. Martin, A. Plastino, and M. A. Fuentes. 2007. Distinguishing noise from chaos. Physical Review Letters 99, 15 (2007), 154102.

[43]

Osvaldo Anibal Rosso, Felipe Olivares, and Angelo Plastino. 2015. Noise versus chaos in a causal Fisher-Shannon plane. Papers in Physics 7, (2015), 070006.

[44]

Osvaldo A. Rosso, Felipe Olivares, Luciano Zunino, Luciana De Micco, André L. L. Aquino, Angelo Plastino, and Hilda A. Larrondo. 2013. Characterization of chaotic maps using the permutation Bandt-Pompe probability distribution. The European Physical Journal B 86, 4 (2013), 116.

[45]

Osvaldo A. Rosso, Raydonal Ospina, and Alejandro C. Frery. 2016. Classification and verification of handwritten signatures with time causal information theory quantifiers. PLOS ONE 11, 12 (2016), e0166868.

[46]

O. A. Rosso, L. Zunino, D. G. Pérez, A. Figliola, H. A. Larrondo, M. Garavaglia, M. T. Martín, and A. Plastino. 2007. Extracting features of Gaussian self-similar stochastic processes via the Bandt-Pompe approach. Physical Review E 76, 6 (2007), 061114.

[47]

P. Sanchez-Moreno, R. J. Yanez, and J. S. Dehesa. 2009. Discrete densities and fisher information. In Difference Equations and Applications. 291–298.

[48]

Patrick Schäfer. 2015. The BOSS is concerned with time series classification in the presence of noise. Data Mining and Knowledge Discovery 29, 6 (2015), 1505–1530.

Digital Library

[49]

Omer Berat Sezer, Erdogan Dogdu, and Ahmet Murat Ozbayoglu. 2018. Context-aware computing, learning, and big data in internet of things: A survey. IEEE Internet of Things Journal 5, 1 (2018), 1–27.

[50]

Taciano Sorrentino, C. Quintero-Quiroz, A. Aragoneses, M. C. Torrent, and Cristina Masoller. 2015. Effects of periodic forcing on the temporally correlated spikes of a semiconductor laser with feedback. Optics Express 23, 5 (2015), 5571.

[51]

Jinjun Tang, Yinhai Wang, and Fang Liu. 2013. Characterizing traffic time series based on complex network theory. Physica A: Statistical Mechanics and its Applications 392, 18 (2013), 4192–4201.

[52]

R. Core Team. 2018. A Language and Environment for Statistical Computing. (2018), https://www.R-project.org.

[53]

Chris Thornton. 1998. Separability is a learner’s best friend. In Proceedings of the 4th Neural Computation and Psychology Workshop, (London, U.K., April 9–11, 1997), John A. Bullinaria, David W. Glasspool, and George Houghton (Eds.). Springer, London, 40–46.

[54]

Chun-Wei Tsai, Chin-Feng Lai, Ming-Chao Chiang, and Laurence T. Yang. 2014. Data mining for Internet of Things: A survey. IEEE Communications Surveys & Tutorials 16, 1 (2014), 77–97.

[55]

Giora Unger and Benny Chor. 2010. Linear separability of gene expression data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics 7, 2 (2010), 375–381.

Digital Library

[56]

Lei Wang and Lei Wang. 2008. Feature selection with kernel class separability. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 9 (2008), 1534–1546.

Digital Library

[57]

Yulei Wu. 2021. Robust learning-enabled intelligence for the Internet of Things: A survey from the perspectives of noisy data and adversarial examples. IEEE Internet of Things Journal 8, 12 (2021), 9568–9579.

[58]

Jie Zhang, Junfeng Sun, Xiaodong Luo, Kai Zhang, Tomomichi Nakamura, and Michael Small. 2008. Characterizing pseudoperiodic time series through the complex network approach. Physica D 237, 22 (2008), 2856–2865.

[59]

Jiayang Zhang, Jie Zhou, Ming Tang, Heng Guo, Michael Small, and Yong Zou. 2017. Constructing ordinal partition transition networks from multivariate time series. Scientific Reports 7, 1 (2017), 7795.

[60]

Hongwen Zheng and Yanxia Zhang. 2008. Feature selection for high-dimensional data in astronomy. Advances in Space Research 41, 12 (2008), 1960–1964.

[61]

L. Zunino, Miguel C. Soriano, and O. A. Rosso. 2012. Distinguishing chaotic and stochastic dynamics from time series by using a multiscale symbolic approach. Physical Review E 86, 4 (2012), 46210.

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A Classification Strategy for Internet of Things Data Based on the Class Separability Analysis of Time Series Dynamics

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Published In

cover image ACM Transactions on Internet of Things

ACM Transactions on Internet of Things Volume 3, Issue 3

August 2022

251 pages

EISSN:2577-6207

DOI:10.1145/3514184

Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 13 July 2022

Online AM: 09 May 2022

Accepted: 01 April 2022

Revised: 01 December 2021

Received: 01 April 2021

Published in TIOT Volume 3, Issue 3

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CAPES, CNPq, FAPEMIG
São Paulo Research Foundation (FAPESP)

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Cited By

Cardoso-Pereira IBorges JViana ALoureiro ARamos H(2024)POPAyI: Muscling Ordinal Patterns for Low-Complex and Usability-Aware Transportation Mode DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.335763211:10(17170-17183)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3357632
Inan MLiao KShen HJayaraman PGeorgakopoulos DTang M(2024)DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor DataMobile and Ubiquitous Systems: Computing, Networking and Services10.1007/978-3-031-63989-0_6(119-135)Online publication date: 19-Jul-2024
https://doi.org/10.1007/978-3-031-63989-0_6
Rey AFrery ALucini MGambini JChagas ERamos H(2023)Asymptotic Distribution of Certain Types of Entropy under the Multinomial LawEntropy10.3390/e2505073425:5(734)Online publication date: 28-Apr-2023
https://doi.org/10.3390/e25050734
Weber LLenz R(2023)Machine learning in sensor identification for industrial systemsit - Information Technology10.1515/itit-2023-005165:4-5(177-188)Online publication date: 9-Oct-2023
https://doi.org/10.1515/itit-2023-0051

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