Incremental hierarchical clustering driven automatic annotations for unifying iot streaming data

  1. Sivadi Balakrishna 1
  2. M.Thirumaran 1
  3. Vijender Kumar Solanki 2
  4. Edward Rolando Núñez-Valdez 3
  1. 1 Pondicherry Engineering College
  2. 2 CMR Institute of Technology, Hyderabad, TS (India)
  3. 3 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Journal:
IJIMAI

ISSN: 1989-1660

Year of publication: 2020

Volume: 6

Issue: 2

Pages: 56-70

Type: Article

DOI: 10.9781/IJIMAI.2020.03.001 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: IJIMAI

Sustainable development goals

Abstract

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data.

Bibliographic References

  • G. Xiao, J. Guo, L. D. Xu, and Z. Gong, “User interoperability with heterogeneous IoT devices through transformation,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1486– 1496, 2014.
  • Rohit Dhall & Vijender Kumar Solanki, “An IoT Based Predictive Connected Car Maintenance Approach,” International Journal of Interactive Multimedia and Artificial Intelligence, ISSN 1989-1660,Vol 4, no 3, pp 1-13, 2017.
  • Sivadi Balakrishna, M Thirumaran, R. Padmanaban, and Vijender Kumar Solanki “An Efficient Incremental Clustering based Improved K-Medoids for IoT Multivariate Data Cluster Analysis”, Peer-to-Peer Networking and Applications, Springer, Vol 13, no 3, pp 1-23, 2019.
  • Sivadi Balakrishna, M Thirumaran, and Vijender Kumar Solanki “Machine Learning based Improved GMM Mechanism for IoT Real-Time Dynamic Data Analysis”, Journal of Revista Ingeniería Solidaria, Vol 16, No 30, e-ISSN 2357-6014, pp 1-29, 2020.
  • H. T. Lin, “Implementing Smart Homes with Open Source Solutions”, International Journal of Smart Home Vol.7 Issue. 4, pp 289–295, 2013.
  • Antunes, Mário, Diogo Gomes, and Rui L. Aguiar. “Towards IoT data classification through semantic features.” Future Generation Computer Systems, Vol. 8 no 6, pp 792-798, 2018.
  • M. Junling, J. Xueqin, and L. Hongqi, “Research on Semantic Architecture and Semantic Technology of IoT,” Research and Development, vol. 8, no. 5, pp. 26–31, 2014.
  • Q. Xu, P. Ren, H. Song, and Q. Du, “Security enhancement for IoT communications exposed to eavesdroppers with uncertain locations,” IEEE Access, vol. 4, pp. 2840–2853, 2016.
  • D. Rong, “The Research on Automatic Semantic Annotation Methods”, Lanzhou University of Technology, Lanzhou, China, 2012.
  • F. Chen, C. Lu, H.Wu. Wu, and M. Li, “A semantic similarity measure integrating multiple conceptual relationships for web service discovery,” Expert Systems with Applications, vol. 6 Issue.7, pp. 19–31, 2017.
  • C. De Maio, G. Fenza, M. Gallo, V. Loia, and S. Senatore, “Formal and relational concept analysis for fuzzy-based automatic semantic annotation,” Applied Intelligence, vol. 40, no. 1, pp. 154– 177, 2014.
  • P. Barnaghi, W. Wang, L. Dong, and C. Wang, “A linked-data model for semantic sensor streams,” IEEE International Conference on and IEEE Cyber, Physical and Social Computing, Green Computing and Communications (GreenCom ’13), Beijing, China, pp. 468–475August - 70 - International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 6, Nº 2 2013.
  • S. Kolozali, M. Bermudez-Edo, D. Puschmann, F. Ganz, and P. Barnaghi, “A knowledge-based approach for real-time IoT data stream annotation and processing,” in Proc: International Conference on Internet of Things, IEEE, pp. 215–222, 2014.
  • W. Wei and P. Barnaghi, “Semantic annotation and reasoning for sensor data,” in Smart Sensing and Context, vol. 5741 of Lecture Notes in Computer Science, pp. 66–76, Springer, Berlin, Germany, 2009.
  • P. Chenyi, Service-oriented entity semantic annotation in internet of things, South China University of Technology, Guangzhou, China, 2015.
  • J. Bing, “Research on semantic-based service architecture and key algorithms for the internet of things”, Jilin University, Changchun, China, 2013.
  • Z. Ming, “Research on several key issues in internet of things applications”, Beijing University of Posts and Telecommunications, Beijing, China, 2014.
  • E. Charton, M. Gagnon, and B. Ozell, “Automatic semantic web annotation of named entities,” in Advances in Artificial Intelligence, vol. 6657 of Lecture Notes in Comput. Sci., Springer, Berlin, Germany, pp. 74–85, 2011.
  • G. Diallo, M. Simonet, and A. Simonet, “An approach to automatic ontology-based annotation of biomedical texts,” Lecture Notes in Computer Science, vol. 40 no. 31, pp. 1024–1033, 2006.
  • M. Jacoby, A. Antonic, K. Kreiner, R. Lapacz, J. Pielorz. “Semantic interoperability as key to IoT platform federation,” in LNCS 10218: Interoperability and Open- Source for the Internet of Things, pp. 3-19, 2017.
  • A.P. Plageras, K.E. Psannis, C. Stergiou, H. Wang, B.B. Gupta, “ Efficient IoT- based sensor BIG Data collection- processing and analysis in Smart Buildings”, Future Generation Computer Systems, 82, pp 349-357, 2018.
  • A. E. Khaled, S. Helal, “Interoperable communication framework for bridging RESTful and topic-based communication in IoT”, Future Generation Computer Systems, Elsevier, 92, pp 628-643, 2019.
  • Kolozali, S. Puschmann, D.; Bermudez-Edo, M.; Barnaghi, P. “On the Effect of Adaptive and Non adaptive Analysis of Time-Series Sensory Data”, IEEE Internet Things J., 3, pp 1084–1098, 2016.
  • Mazayev, Andriy, Jaime A. Martins, and Noélia Correia. “Interoperability in IoT through the Semantic Profiling of Objects.” IEEE Access 6, pp 19379-19385, 2017.
  • Mayer, Simon, Jack Hodges, Dan Yu, Mareike Kritzler, and Florian Michahelles. “An open semantic framework for the industrial Internet of Things.” IEEE Intelligent Systems 32, no. 1, pp 96-101, 2017.
  • Shi, Feifei, Qingjuan Li, Tao Zhu, and Huansheng Ning. “A survey of data semantization in internet of things.” Sensors 18, no. 1, 313, 2018.
  • Al Zamil, Mohammed Gh, Majdi Rawashdeh, Samer Samarah, M. Shamim Hossain, Awny Alnusair, and Sk Md Mizanur Rahman. “An annotation technique for in-home smart monitoring environments.” IEEE Access 6, pp 1471-1479, 2018.
  • Moutinho, Filipe, Luís Paiva, Julius Köpke, and Pedro Maló. “Extended Semantic Annotations for Generating Translators in the Arrowhead Framework.” IEEE Transactions on Industrial Informatics 14, no. 6, pp 2760-2769. 2018.