RSSI Fingerprint Indoor Modeling using Near Ground Electromagnetic Wave Propagation Approach for Intruder Detection in Wireless Sensor Networks

In Wireless Sensor Networks (WSN), intruder detection is a crucial aspect to ensure the security and reliable operation of the network. Intruder detection in WSN is typically used to identify and classify intruders based on radio frequency (RF) fingerprints in a wireless sensor network environment. The fingerprint method involves capturing and analyzing the RF signals sent by intruders as they transmit data in the monitored area. The radio frequency characteristics of each intruder cause different changes to the RF signal pattern. Despite its potential, WSN radio frequency fingerprint intruder detection faces challenges such as signal interference and environmental factors affecting RF propagation. In this paper, we developed a new Received Signal Strength Indicator (RSSI) fingerprint model based on the Near Ground Electromagnetic Wave Propagation model. Using this approach, we built an RSSI fingerprint model capable of detecting intruders. By modifying the constant of the free space propagation model from 32.45 dB to 58.06 dB when there is no intruder and 57.64 dB when there is an intruder, we were able to enhance detection accuracy. Additionally, we considered using the standard deviation parameter for intruder detection. If we observe a standard deviation value of 1.17, it indicates no intruder, whereas a standard deviation value of 0.75 indicates the presence of an intruder.