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An Enhanced Mobile Device-Based Navigation Model: Ubiquitous Computing

An Enhanced Mobile Device-Based Navigation Model: Ubiquitous Computing

Emmanuel Ajayi Olajubu, John E. Efiong, Aderounmu G. Adesola
Copyright: © 2018 |Volume: 9 |Issue: 1 |Pages: 20
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781522543282|DOI: 10.4018/IJMCMC.2018010101
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MLA

Olajubu, Emmanuel Ajayi, et al. "An Enhanced Mobile Device-Based Navigation Model: Ubiquitous Computing." IJMCMC vol.9, no.1 2018: pp.1-20. http://doi.org/10.4018/IJMCMC.2018010101

APA

Olajubu, E. A., Efiong, J. E., & Adesola, A. G. (2018). An Enhanced Mobile Device-Based Navigation Model: Ubiquitous Computing. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 9(1), 1-20. http://doi.org/10.4018/IJMCMC.2018010101

Chicago

Olajubu, Emmanuel Ajayi, John E. Efiong, and Aderounmu G. Adesola. "An Enhanced Mobile Device-Based Navigation Model: Ubiquitous Computing," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 9, no.1: 1-20. http://doi.org/10.4018/IJMCMC.2018010101

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Abstract

This article formulated, simulated and evaluated an enhanced model for a non-linear/non-Gaussian integrated Global Positioning System (GPS) and Inertial Navigation System (INS) mobile-based device navigation system using the Particle Filter (PF). This was with a view to enhancing the accuracy and minimize the delay experienced in the existing system which relies on linear data. An android driven Infinix X5 mobile device with GPS and INS-based sensors was used to implement the model formulated, standard Bayesian estimators were used to generate non-linear datasets with Gaussian/non-Gaussian white noises for mobile device based INS/GPS sensors. A mathematical model was formulated using Sampling Importance-weight Resampling (SIR) algorithm of the PF. The conceptual model was developed using Simulink and the design specification was done using Unified Modeling Language (UML). The model was simulated with MATLAB and the simulation results obtained were evaluated using standard metrics, and benchmarked with existing model. The overall results showed that the proposed model performed better than existing ones in term of accuracy. However, the model did not impact on delay reduction.

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