Reliable RSS Indoor Location Systems Based on Signal Distribution

International Journal of Industrial Engineering
© 2024 by SSRG - IJIE Journal
Volume 11 Issue 3
Year of Publication : 2024
Authors : Hao Yang
pdf
How to Cite?

Hao Yang, "Reliable RSS Indoor Location Systems Based on Signal Distribution," SSRG International Journal of Industrial Engineering, vol. 11,  no. 3, pp. 7-10, 2024. Crossref, https://doi.org/10.14445/23499362/IJIE-V11I3P102

Abstract:

With the in-depth development of the Internet and information technology's rapid development in today's world, location-based services are gradually entering people's daily lives. Indoor positioning has gradually penetrated all aspects of social life. However, the current indoor positioning technology still has significant shortcomings in positioning accuracy. This paper will explore indoor localisation methods to increase the accuracy of indoor localization, reduce the cost of positioning, and decrease the power consumption of equipment. In this paper, we propose a more stable based on RSS fingerprint localization algorithm for signal distribution and design a reliable RSS indoor positioning system, Wi-SD. Experiments show that Wi-SD has better stability and accuracy.

Keywords:

Positioning, Signal distribution, Similarity matching, RSS, Fingerprint.

References:

[1] Ajay K. Gupta, and Udai Shanker, “Caching in Location Based Services: Approaches, Challenges and Emerging Trends,” Wireless Personal Communications, vol. 135, no. 3, pp. 1581-1615, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Noemi Mauro et al., “Using Consumer Feedback from Location-Based Services in Poi Recommender Systems for People with Autism,” Expert Systems with Application, vol. 199, no. 8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Srikanth Beldona, Hemant V. Kher, and Kunwei Lin, “Gains-Focused Vs Risk-Averse Orientations and Their Impact on Location-Based Marketing Services in Tourism,” Journal of Hospitality and Tourism Technology, vol. 13, no. 2, pp. 333-347, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Gianfranco Lombardo et al., “Digital Twin for Continual Learning in Location Based Services,” Engineering Applications of Artificial Intelligence, vol. 127, pp. 107203-107215, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Vijay Kumar Yadav, “Anonymous and Linkable Ring Signcryption Scheme for Location-Based Services in VANETs,” Vehicular Communications, vol. 45, no. 2, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] T. Maclay, W. Everetts, and D. Engelhardt, “Responsible Satellite Design and Operational Practices: A Critical Component of Effective Space Environment Management (SEM),” Journal of Space Safety Engineering, vol. 8, no. 2, pp. 150-154, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Abdelkabir Lahrech, and Aziz Soulhi, “Vehicle Positioning in Urban Environments Using Particle Filtering-Based Global Positioning System, Odometry, And Map Data Fusion,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 4, pp. 3924-3938, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Dominik Csik, Ákos Odry, and Peter Sarcevic, “Fingerprinting-Based Indoor Positioning Using Data Fusion of Different Radiocommunication-Based Technologies,” machines, vol. 11, no. 2, pp. 1-8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Abdulraqeb Alhammadi, Zaid Ahmed Shamsan, and Arijit De, “Enhancing Indoor User Localization: An Adaptive Bayesian Approach for Multi-Floor Environments,” Computers, Materials & Continua, vol. 80, no. 2, pp. 1889-1905, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Atefe Alitaleshi, Hamid Jazayeriy, and Javad Kazemitabar, “EA-CNN: A Smart Indoor 3D Positioning Scheme Based on Wi-Fi Fingerprinting and Deep Learning,” Engineering Applications of Artificial Intelligence, vol. 117, pp. 105509-105521, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Irina Strelkovskaya, Irina Solovskaya, and Juliya Strelkovska, “Improving the Accuracy of User Location in the Wi-Fi Network Using Complex Spline-Functions,” International Scientific and Technical Conference Modern Challenges in Telecommunications, Springer, Cham, pp. 317-331, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Son Minh Nguyen, Duc Viet Le, and Paul J.M. Havinga, “Seeing the World from Its Words: All-Embracing Transformers for Fingerprint Based Indoor Localization,” Pervasive and Mobile Computing, vol. 100, no. 5, pp. 101912-101922, 2024.[CrossRef] [Google Scholar] [Publisher Link]