DL-IPS: Deep Learning Based Indoor Positioning System for Improved Accuracy
Bhulakshmi Bonthu1, Subaji M2
1Bhulakshmi Bonthu , School of Computer Science and engineering, VIT Vellore Institute of Technology, Vellore, India.
2Subaji M, Institute for Industry and International Programmes, VIT Vellore Institute of Technology, Vellore, India.

Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 10797-10801 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4351118419/2019©BEIESP | DOI: 10.35940/ijrte.D4351.118419

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Abstract: Indoor tracking has evolved with various methods and well known these days. There are diverse types of solutions that concentrate on exactness, low cost, and control utilization within the field. Particularly in recent years, Received Signal Strength Indicator based positioning estimation have been getting popular. Still, the accuracy are not adequate, and there’s no correct way chosen to overcome this issue. In this paper, we propose a strategy that leverage Deep Learning and Wi-Fi/BLE (Bluetooth Low Energy) Fingerprinting strategy to produce superior precise accuracy.
Keywords: Deep Learning, Fingerprinting, Indoors Positioning, Localization, Wi-Fi Positioning, BLE, Machine Learning
Scope of the Article: Deep Learning.