Unveiling the Invisible: Wi-Fi-Enabled Wall Penetration through Machine Learning

Registration ID: IJNRD_207218

Published ID: IJNRD2310232

DOI: Click Here to Get

Navjot Singh , Abhilash Gaurav

Wi-Fi, wall penetration, machine learning, wire- less technology, signal analysis, radio waves, remote sensing, environmental monitoring.

Generally speaking, Wi-Fi signals act as information conduits between a transmitter and a receiver. In this essay, we demonstrate how Wi-Fi may also broaden our sensory perception, allowing us to perceive moving objects behind open doors and across walls. Particularly, we can count the number of individuals in an open room and their locations by using comparable signals. Additionally, without carrying any transmitting equipment, we are able to recognise uncomplicated movements made behind a wall and integrate them into a sequence to relay dispatches to a wireless receiver. Two major inventions are presented in this article. The proposed method takes benefit of the fact that “Wi-Fi signals” can penetrate most materials, including walls, and can be reflected by objects and surfaces behind them. By analyzing the variations in the “Wi-Fi signal” patterns caused by the objects behind the wall, it is possible to create a 3D representation of the objects and their locations. The paper describes the experimental setup used to test the proposed method and the results obtained. The experiments involved using tainted-the-shelf “Wi-Fi” equipment and custom software to capture and process the Wi-Fi signals. The outcomes demonstration that the planned technique can accurately detect and locate objects behind walls, including human subjects. The potential applications of this technology are numerous, ranging from search and rescue operations to home security and surveillance. However, the paper also discusses the ethical and privacy concerns associated with using Wi-Fi signals to see through walls and emphasizes the need for responsible use of this technology. Overall, the research presented in this paper represents a significant step towards the development of practical and ethical Wi-Fi-based through-wall imaging systems

Published Paper   E-Certificate

Navjot Singh & Abhilash Gaurav (October-2023). Unveiling the Invisible: Wi-Fi-Enabled Wall Penetration through Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(10), c283-c288. https://ijnrd.org/papers/IJNRD2310232.pdf

Volume 8 Issue 10, October-2023

Pages : c283-c288

Paper Reg. ID: IJNRD_207218

Published Paper Id: IJNRD2310232

Downloads: 000121982

Research Area: Engineering

Country: Ludhiana, Punjab, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2310232.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2310232

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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