An Intelligent Flooring System for Intrusion Detection and Automated Security Response
Abstract
This paper presents a project titled An Intelligent Flooring System for Instruction Detection and Automated Security Response. The main aim of this system is to improve building security by using smart floor sensors to detect human movement and automatically respond to security events. Traditional security systems such as cameras and motion detectors depend on human monitoring and may fail in low light or emergency situations. The proposed system uses pressure and force sensors placed under the floor to detect footsteps, walking patterns, and unusual movements. The system analyzes this sensor data to recognize specific instructions or actions and then activates automatic security responses such as alarms or alerts. This approach provides better privacy, faster response time, and improved safety. The system is designed to be simple, cost-effective, and suitable for smart buildings and modern security applications.
References
G. K. Sreeram et al., "IoT-Enabled Anti-Theft Flooring Mat for Shop Security," IEEE, 2024.
V.S.Raj & S.V Ramana,"Intelligent security system for residential and industrial automation," IEEE, 2024.
G. K. Sreeram, S. V. Krishna, G. A. Aravind, and B. S. Prasad, "IoT-Enabled Anti-Theft Flooring Mat for Shop Security," Proc. IEEE 4th Int. Conf. on Smart Technologies for Power, Energy and Control, Bengaluru, India, Dec. 2022.
Q. Shi, Z. Zhang, T. He et al., “Deep Learning Enabled Smart Mats as a Scalable Floor Monitoring System,” Nature Communications, vol. 11, no. 4609, 2020.
L. Minvielle, M. Atiq, R. Serra, M. Mougeot, and N. Vayatis, “Fall Detection Using Smart Floor Sensor and Supervised Learning,” Proc. IEEE Engineering in Medicine and Biology Society (EMBC), 2017.
J. Liang et al., “Smart Floor Tiles for Crowd Monitoring Using Battery-Free Sensors,” IEEE Sensors Letters, 2024.
“Multilayered Triboelectric Energy Harvester as a Smart Floor Mat,” IEEE Conference Publication, 2022.
P. Ganesan and S. A. E. Xavier, “An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier,” Intelligent Automation & Soft Computing, vol. 35, no. 3, pp. 2979–2996, 2023.
J. L. Leevy and T. M. Khoshgoftaar, “A Survey and Analysis of Intrusion Detection Models,” Journal of Big Data, vol. 7, no. 104, 2020.
17.10) W. W. Lo, S. Layeghy, M. Sarhan et al., “E-GraphSAGE: A Graph Neural Network Based Intrusion Detection System for IoT,” 2021.
Refbacks
- There are currently no refbacks.