1
Visiting Professor, University of Technology Sydney, Sydney, Australia
(Mohammad.Alshehri@uts.edu.au)
Abstract :
Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.
Keywords :
Indoor localization , Smartphones , Tracking model , GSO algorithm , Kalman filter , Estimation error
References :
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| Style | # |
|---|---|
| MLA | Mohammad Alshehri. "An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm." Journal of Cybersecurity and Information Management, Vol. 6, No. 2, 2021 ,PP. 101-107 (Doi : https://doi.org/10.54216/JCIM.060203) |
| APA | Mohammad Alshehri. (2021). An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm. Journal of Journal of Cybersecurity and Information Management, 6 ( 2 ), 101-107 (Doi : https://doi.org/10.54216/JCIM.060203) |
| Chicago | Mohammad Alshehri. "An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm." Journal of Journal of Cybersecurity and Information Management, 6 no. 2 (2021): 101-107 (Doi : https://doi.org/10.54216/JCIM.060203) |
| Harvard | Mohammad Alshehri. (2021). An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm. Journal of Journal of Cybersecurity and Information Management, 6 ( 2 ), 101-107 (Doi : https://doi.org/10.54216/JCIM.060203) |
| Vancouver | Mohammad Alshehri. An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm. Journal of Journal of Cybersecurity and Information Management, (2021); 6 ( 2 ): 101-107 (Doi : https://doi.org/10.54216/JCIM.060203) |
| IEEE | Mohammad Alshehri, An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm, Journal of Journal of Cybersecurity and Information Management, Vol. 6 , No. 2 , (2021) : 101-107 (Doi : https://doi.org/10.54216/JCIM.060203) |