Detection of short-term radio signal disturbances in industrial wireless sensor networks

Abstract

The performance of wireless sensor networks (WSNs) in industrial applications may be degraded due to harsh radio signal propagation conditions. Mobile heavy machines and large metal and concrete surfaces usually worsen the signal fading effects resulting in deep fades. We have studied the effects of certain industrial environmental disturbances on the quality of the transmitted signals in real-life industrial environments. We used software defined radio (SDR) as the receiver to capture the transmitted signals and analyzed the statistical properties of the captured signals with Matlab/Simulink tools. Our studies led us to a method which aims to identify the transmission problems related to signal multipath propagation by calculating the probability density function (PDF) of the received signal and comparing it to the simulated theoretical Rician distributions. The results show that tracking the transformations of the PDF of the received signal helps to recognize the temporal changes in radio link environment and in that way can help to choose the right actions in order to overcome the communication problems.