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A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT

1 College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China
2 Xiamen Engineering Research Centre of Enterprise Interoperability and Business Intelligence, Xiamen, 361021, China

Special Issues: Internet of Things (IoT)-Based Environmental Intelligence

In smart environments based on the Internet of Things (IoT), almost all of the object information that is collected by various sensors is time series data, which records the behavior of the objects. Analyzing the correlation between different time series data, other than those in the same time series, is more helpful to discovering their behavioral relations. This has become one of the important current issues in the IoT. To analyze the correlation, a clustering algorithm named the CPCCM (clustering algorithm based on precise correlation coefficient matching) is presented. First, each initial sequence is split into a set of subsequences by adopting a preset sliding window. Then, the correlation coefficients between any pair of subsequence sets from two sequences are resolved. Those pairs that pass some preset Pearson correlation coefficient threshold are clustered. In the CPCCM, a cross-traversal strategy is introduced to improve the search efficiency. The cross-traversal strategy alternatively searches the subsequences in two subsequence sets. To improve the clustering efficiency, in each initial sequence, adjacent subsequences are merged into longer subsequences and replaced by it if they appear in the same subsequence set. Finally, by analyzing practical electric power consumption data, the CPCCM is shown to be promising and able to be applied in similar scenarios. By comparison with the agglomerative hierarchical clustering algorithm, the major contributions of this work is that the clustering quality is improved by using the strategy of precise matching and cross-traversal, and complexity of the algorithm is reduced by merging adjacent subsequences. Therefore, CPCCM can be applied to analyze behavior between different objects in smart environments.
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Keywords Internet of Things; time series; pearson correlation coefficient; clustering; precise matching

Citation: Haibo Li, Juncheng Tong. A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT. Mathematical Biosciences and Engineering, 2019, 16(6): 6654-6671. doi: 10.3934/mbe.2019331

References

  • 1. M. Weiser, R. Gold and J. S. Brown, The origins of ubiquitous computing research at PARC in the late 1980s, IBM Syst. J., 38 (1999), 693–696.
  • 2. V. A. Memos, K. E. Psannis, Y. Ishibashi, et al., An efficient algorithm for media-based surveillance system (EAMSuS) in IoT smart city framework, Future Gener. Comp. Syst., 83 (2018), 619–628.
  • 3. S. Tang, D. R. Shelden, C. M. Eastman, et al., A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends, Automat. Const., 101 (2019), 127–139.
  • 4. T. Baker, A. Taleb-Bendiab, M. Randles, et al., Understanding elasticity of cloud services compositions, In 2012 IEEE Fifth International Conference on Utility and Cloud Computing, Chicago(USA), IEEE, (2012), 231–232.
  • 5. A. Jula, E. Sundararajan and Z. Othman, Cloud computing service composition: A systematic literature review, Expert Syst. Appl., 41 (2014), 3809–3824.
  • 6. Q. Wu, G. Ding, Y. Xu, et al., Cognitive internet of things: a new paradigm beyond connection, IEEE Int. Things J., 1 (2014), 129–143.
  • 7. C. Gomez, S. Chessa, A. Fleury, et al., Internet of Things for enabling smart environments: A technology-centric perspective, J. Ambient Int. Smart Environ., 11 (2019), 23–43.
  • 8. L. Atzori, A. Iera and G. Morabito, The internet of things: A survey, Comput. Netw., 54 (2010), 2787–2805.
  • 9. F. Chen, P. Deng, J. F. Wan, et al., Data Mining for the Internet of Things. Literature Review and Challenges, Int. J. Distrib. Sensor Netw., 11 (2015), P431047.
  • 10. H. Li, Z. Zhang, X. Wang, et al., Electricity consumption behaviour analysis based on time sequence clustering, In 2018 International Conference on Computer Information Engineering and Bioinformatics, Guangzhou(China), IOP Publishing, (2018), 032011.
  • 11. S. Pravilovic, M. Bilancia, A. Appice, et al., Using multiple time series analysis for geosensor data forecasting, Inf. Sci., 380 (2017), 31–52.
  • 12. J. Liu, W. Li, J. Wu, et al., Visualizing the intercity correlation of PM2. 5 time series in the Beijing-Tianjin-Hebei region using ground-based air quality monitoring data, PloS One, 13 (2018), e0192614.
  • 13. J. Soares, P. A. Makar, Y. Aklilu, et al., The use of hierarchical clustering for the design of optimized monitoring networks, Atmos. Chem. Phys., 18 (2018), 6543–6566.
  • 14. A. Zaslavsky, C. Perera and D. Georgakopoulos, Sensing as a service and big data, In International Conference on Advances in Cloud Computing (ACC-2012), Bangalore(India), Eprint Arxiv, (2012), 21–29.
  • 15. C. Chang and C. Li, Algebraic secret sharing using privacy homomorphisms for IoT-based healthcare systems, Math. Biosci. Eng., 16 (2019), 3367–3381.
  • 16. Y. Ren, Y. Leng, Y Cheng, et al., Secure data storage based on blockchain and coding in edge computing, Math. Biosci. Eng., 16 (2019), 1874–1892.
  • 17. C. Li and B. Palanisamy, Privacy in internet of things: From principles to technologies, IEEE Int. Things J., 6 (2019), 488–505.
  • 18. A. P. Plageras, K. E. Psannis, C. Stergiou, et al., Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings, Future Gener. Comp. Syst., 82 (2018), 349–357.
  • 19. K. P. Kibiwott, Y. Zhao, J. Kogo, et al., Verifiable fully outsourced attribute-based signcryption system for IoT eHealth big data in cloud computing, Math. Biosci. Eng., 16 (2019), 3561–3594.
  • 20. S. K. Jensen, T. B. Pedersen and C. Thomsen, Time series management systems: A survey, IEEE T. Knowledge Data Eng., 29 (2017), 2581–2600.
  • 21. C. Stergiou, K. E. Psannis, A. P. Plageras, et al., Algorithms for efficient digital media transmission over IoT and cloud networking, J. Multimedia Inf. Syst., 5 (2018), 27–34.
  • 22. K. E. Psannis, C. Stergiou and B. B. Gupta, Advanced media-based smart big data on intelligent cloud systems, IEEE T. Sustain. Comput., 4 (2018), 77–87.
  • 23. W. Ejaz, M. Naeem, A. Shahid, et al., Efficient energy management for the internet of things in smart cities, IEEE Commun. Mag., 55 (2017), 84–91.
  • 24. A. F. Mohammad and V. Korosh, Energy management-as-a-service over fog computing platform, IEEE Int. Things J., 3 (2015), 161–169.
  • 25. F. Adenugba, S. Misra, R. Maskeliūnas, et al., Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach, Math. Biosci. Eng., 16 (2019), 5490–5503.
  • 26. M. Izal, D. Morató, E. Magaña, et al., Computation of traffic time series for large populations of IoT devices, Sensors, 19 (2019), 78.
  • 27. Ş. Kolozali, D. Puschmann, M. Bermudez-Edo, et al., On the effect of adaptive and nonadaptive analysis of time-series sensory data, IEEE Int. Things J., 3 (2016), 1084–1098.
  • 28. R. Salles, P. Mattos, A. M. D. Iorgulescu, et al., Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean. Ecol. Inform., 36 (2016), 94–105.
  • 29. J. Roberts, M. Curran, S. Poynter, et al., Correlation confidence limits for unevenly sampled data, Comput. Geosci., 104 (2017), 120–124.
  • 30. I. Ozken, D. Eroglu, S. F. Breitenbach, et al., Recurrence plot analysis of irregularly sampled data, Phys. Rev. E., 98 (2018), 052215.
  • 31. H. Li, K. C. C. Chan, M. Liang, et al., Composition of resource-service chain for cloud manufacturing, IEEE T. Ind. Informat., 12 (2016), 211–219.
  • 32. H. Li, M. Liang and T. Liang, Optimizing the composition of a resource service chain with inter-organizational collaboration, IEEE T. Ind. Informat., 13 (2017), 1152–1161.
  • 33. H. Li and T. He, Selecting key feature sequence of resource services in industrial internet of things, IEEE Access, 6 (2018), 72152–72162.
  • 34. L. Wen, L. Gao, Y. Dong, et al., A negative correlation ensemble transfer learning method for fault diagnosis based on convolutional neural network, Math. Biosci. Eng., 16 (2019), 3311–3330.
  • 35. Z. Zhang, L. Liu, S. Zhang, et al., A service-based method for multiple sensor streams aggregation in fog computing, Wireless Commun. Mobile Comput., 1 (2018), 1–11.
  • 36. M. Mehdizadeh, R. Ghazi and M. Ghayeni, Power system security assessment with high wind penetration using the farms models based on their correlation, IET Renew. Power Gener., 12 (2018), 893–900.
  • 37. Z. Chen, Z. Xue, L. Zhang, et al., Analyzing the correlation and predictability of wind speed series based on mutual information, IEEE T. Electr. Electr. Eng., 13 (2018), 1829–1830.
  • 38. J. Olauson and M. Bergkvist, Correlation between wind power generation in the European countries, Energy, 114 (2016), 663–670.
  • 39. F. Wang, A novel coefficient for detecting and quantifying asymmetry of California electricity market based on asymmetric detrended cross-correlation analysis, Chaos Interdiscipl. J. Nonlinear Sci., 26 (2016), 063109.
  • 40. T. Cui, F. Caravelli and C. Ududec, Correlations and clustering in wholesale electricity markets, Physica A., 492 (2018), 1507–1522.
  • 41. R. Lin, B Wu and Y Su, An adaptive weighted pearson similarity measurement method for load curve clustering, Energies, 11 (2018), 1–17.
  • 42. A. Mueen, H. Hamooni and T. Estrada, Time series join on subsequence correlation, In 2014 IEEE International Conference on Data Mining, Shenzhen(China), IEEE Computer Society Press, (2014), 450–459.
  • 43. Z. Ye, S. Mistry, A. Bouguettaya, et al., Long-term QoS-aware cloud service composition using multivariate time series analysis, IEEE T. Services Comput., 9 (2014), 382–393.
  • 44. M. Disegna, P. D'Urso and F. Durante, Copula-based fuzzy clustering of spatial time series, Spat. Stat., 21 (2017), 209–225.
  • 45. J. C. Dunn, Well-separated clusters and optimal fuzzy partitions, J. Cybernetics, 4 (1974), 95–104.
  • 46. M. Halkidi, Y. Batistakis and M Vazirgiannis, On clustering validation techniques, J. Intell. Inf. Syst., 17 (2001), 107–145.

 

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