Intrusion detection in RFID system using computational intelligence approach for underground mines

Abstract

SummaryThe radio frequency identification technology (RFID) is commonly used for object tracking and monitoring. In this paper, we discuss a model for intrusion detection system based on RFID to identify the abnormal behavior of underground mines' toxic gases. This model consists of various types of sensor nodes that are integrated with RFID tag, which are deployed in the underground mines by using Zigbee protocol. It consists of coordinators, routers, and sensor nodes, according to different capabilities and the probabilities of intrusive activities that occur in underground mines. It can detect the real‐time abnormal behavior of the toxic gases viz. methane, carbon monoxide, carbon dioxide, hydrogen sulfide, and nitrogen dioxide gases, using artificial neural network middleware techniques. It increases the detection accuracy and reduces the false alarm rate, using multilayer perceptron, radial basis function network, and probabilistic and general regression neural network (PNN/GRNN) techniques. The simulations are performed on the toxic gas dataset, which has been generated in a real‐time scenario by using different gas sensors. The real‐time dataset contains intrusive and nonintrusive values of methane, carbon monoxide, carbon dioxide, hydrogen sulfide, and nitrogen dioxide gases. Experimentally, the PNN/GRNN provides higher detection accuracy as 90.153% for carbon monoxide, 86.713% for carbon dioxide, 93.752% for hydrogen sulfide, and 75.472% for nitrogen dioxide. The PNN/GRNN also provides low false alarm rate as 9.85% for carbon monoxide, 13.29% for carbon dioxide, 6.24% for hydrogen sulfide, and 24.53% for nitrogen dioxide compared with the multilayer perceptron and radial basis function networks.