Hybrid Machine Learning Model for Context-Aware Social IoT Using Location-Based Service Under Both Static and Mobility Conditions

  • D. P. AbhishekEmail author
  • Nidhi Dinesh
  • S. P. Shiva Prakash
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)


In recent times, the data traffic generated by the Internet of Things (IoT) devices is increasing. As these data are informative and useful, the categorization of these data helps in its effective and efficient use. Social Internet of Things (SIoT) generates huge data traffic compared to IoT devices and these data need to be manipulated so that the data are used productively. There exist many approaches in the reported work using machine learning models to use data effectively. In this paper, a Hybrid Machine Learning (HML) model is proposed to detect the device movement (location) based on X-, Y-coordinates on private mobile devices and private static devices, by applying Naive Bayes classifier on static data sets and K-Means clustering on mobile data sets. The result shows that the proposed model exhibit more accurate solution compared to the existing approaches.


Hybrid machine learning (HML) Machine learning (ML) Supervised machine learning (SML) Unsupervised machine learning (UML) Naive Bayes algorithm (NB) K-Means algorithm (KM) 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • D. P. Abhishek
    • 1
    Email author
  • Nidhi Dinesh
    • 2
  • S. P. Shiva Prakash
    • 3
  1. 1.Cobalt LabsMysuruIndia
  2. 2.DoS in Computer Science, UoMMysuruIndia
  3. 3.JSS Science and Technology University (Formerly SJCE)MysuruIndia

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