Indoor device-free passive localisation (DfPL) technology uses a received signal strength indication (RSSI)-based method to record variances of a measured signal where a person being tracked is not carrying any electronic device that can be used to estimate the location. The system monitors the changes in the RSSI measurements caused by the presence of a human body in an indoor environment. For example, it is known that the resonance frequency of water is 2.4 GHz and the human body contains >70% water. Thus, the human body attenuates the wireless signal reacting as an absorber. Wireless communication signal strengths between a number of nodes, using IEEE 802.11 or 802.15.4 standards, show that communication links covering distinct areas cannot be affected simultaneously by only one person. Thus, the authors have deployed a novel system that can identify multi-occupants in an environment using patterns of motion from those monitored areas. A pattern recognition neural network was used to identify two people in the environment. No other work based on the DfPL technique has focused on multi-occupancy.
Localisation technique that monitors wireless communications and location dependent signal characteristics is presented in this paper. The human body contains more than 70% water which is causing variances in the Received Signal Strength Indicator (RSSI) measurements. DfP is a technique to detect a person without the need for any physical devices i.e. tags or sensors. This paper focuses on communication protocols such as Radiogram Protocol, Transmission Control Protocol (TCP), and User Datagram Protocol (UDP), outlining the possibility of using these protocols in Wireless Sensor Networks (WSNs). Histograms and historical data are new concepts in a DfP scenario which can improve the accuracy of location estimation in DfP Localisation.
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