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A New Approach to Predict user Mobility Using Semantic Analysis and Machine Learning

  • Mobile & Wireless Health
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Mobility prediction is a technique in which the future location of a user is identified in a given network. Mobility prediction provides solutions to many day-to-day life problems. It helps in seamless handovers in wireless networks to provide better location based services and to recalculate paths in Mobile Ad hoc Networks (MANET). In the present study, a framework is presented which predicts user mobility in presence and absence of mobility history. Naïve Bayesian classification algorithm and Markov Model are used to predict user future location when user mobility history is available. An attempt is made to predict user future location by using Short Message Service (SMS) and instantaneous Geological coordinates in the absence of mobility patterns. The proposed technique compares the performance metrics with commonly used Markov Chain model. From the experimental results it is evident that the techniques used in this work gives better results when considering both spatial and temporal information. The proposed method predicts user’s future location in the absence of mobility history quite fairly. The proposed work is applied to predict the mobility of medical rescue vehicles and social security systems.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, NMAM Institute of Technology, Udupi District, Nitte, Karnataka, 574110, India

    Roshan Fernandes

  2. Department of Computer Science and Engineering, St. Joseph Engineering College, Vamanjoor, Mangalore, Karnataka, 575028, India

    Rio D’Souza G. L.

Authors
  1. Roshan Fernandes
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  2. Rio D’Souza G. L.
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Correspondence to Roshan Fernandes.

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Conflict of Interest

Roshan Fernandes declares that he has no conflict of interest. Dr. Rio G. L. D’Souza declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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This article is part of the Topical Collection on Mobile & Wireless Health

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Fernandes, R., D’Souza G. L., R. A New Approach to Predict user Mobility Using Semantic Analysis and Machine Learning. J Med Syst 41, 188 (2017). https://doi.org/10.1007/s10916-017-0837-x

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  • DOI: https://doi.org/10.1007/s10916-017-0837-x

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