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VI. RESULT ANALYSIS From an energy perspective, future developments could
1. High Accuracy: Achieved an average error of 1-2 focus on designing more sustainable beacons powered by
meters, outperforming traditional indoor navigation renewable energy sources, such as solar or kinetic energy, to
systems. reduce operational costs and increase lifespan. The fusion of
BeaconTrack with other positioning technologies like Wi-Fi
2. Fast Response: Delivered real-time location updates in and UltraWideband (UWB) could further enhance its
under 2 seconds, ensuring smooth user navigation.
accuracy and reliability, creating a hybrid system capable of
3. Energy Efficiency: Beacons lasted up to 30 months due overcoming limitations in diverse scenarios.
to adaptive power-saving mechanisms.
REFERENCES
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6. Versatility: Proved efficient in malls, hospitals, and Placement for Indoor Positioning. IEEE Transactions
airports, offering seamless navigation and contextual on Mobile Computing, 21(3), 456–467. DOI:
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VII. CONCLUSION [3] Smith, J., & Lee, K. (2021). Bluetooth Low Energy
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optimizing indoor navigation systems through the innovative Solutions. Journal of Communication Networks, 19(4),
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Energy (BLE) signals, BeaconTrack achieves high accuracy
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scalable design, energy-efficient beacons, and dynamic International Telecommunication Union.
algorithms make it cost -effective and adaptable for diverse [5] Brown, D. (2023). Augmenting Navigation Systems
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with Machine Learning: A Case Study. Proceedings of
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experience and sets a new standard for indoor positioning [6] Kumar, P., & Singh, A. (2022). Design and
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VIII. FUTURE SCOPE [7] Bluetooth SIG (2021). Bluetooth Low Energy
The future of BeaconTrack holds immense potential for Technology Overview. Retrieved from
advancements in technology and its applications across https://www.bluetooth.com.
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and machine learning can elevate the system by enabling [8] Rossi, M., & Chen, Z. (2023). Triangulation Algorithms
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