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Title: Wi-Fi Tomographic intruder detection security alarm system
Authors: Muller, D.P.
Issue Date: 28-Jul-2021
Abstract: Tomographic Motion Detection (TMD) is a technology that provides complete coverage of an area using radio waves and detects any movement with the ability to be completely hidden from view as it is not a line of sight technology, using signals that penetrate through objects. The monitored area is surrounded by nodes that communicates with each other and detects movements by the disturbance to the Receive Signal Strength (RSS) of each node measured by a processing and controlling unit. 2.4 GHz signals are heavily attenuated by anything containing water such as the human body. Hence using Wi-Fi (Wireless Fidelity) signals which operate in the 2.4 GHz band, it is possible to identify the significant disturbances in the Receive Signal Strength Indicator (RSSI) and identify human movements without carrying additional transmitting devices. Today, protecting our valuable assets, confidential data, human life in a better way and avoiding unauthorised physical access to devices, in the field of Information Security, are demands in need. This is an eye opener for the use of TMD when compared with the limitations with commonly used Passive Infrared (PIR) motion detectors. This research is an attempt to use non-overlapping Wi-Fi channels, simultaneously to generate better TMD for human detection within the Wi-Fi frequency range. Initially, as the required hardware to capture the RSSI, ESP 8266, embedded Wi-Fi modules controlled by Arduino, connected to a computer via Universal Serial Bus (USB) were setup. The Wi-Fi modules can be configured for the required Wi-Fi channel, transmitting or receiving mode and capable of more configurations. Arduino, Integrated Development Environment (IDE) which is open source and MS Comm Control a freeware control are used for developing, uploading the developed programs into the Wi-Fi module microcontrollers and reading RSSI using a computer. Thereafter, identified the significant difference in the RSSI measured for humans, compared with other objects. For the analysis, data from RSSI readings were gathered in several tests placing objects and humans in between the Wi-Fi transmitters and receivers in a straight line obstructing the signals from the transmitters and graphs were generated. Finally, determined how accurately a human movement can be detected, using a probability based approach. Monitored the Wi-Fi, RSSI, with statistical calculations, detected an intruder entering into the defined boundaries and triggered an alarm, constructing a security alarm.
Appears in Collections:2018

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