An artificial neural network approach to the problem of wireless sensors network localization
One of the imperative problems in the realm of wireless sensor networks is the problem of wireless sensors localization. Despite the fact that much research has been conducted in this area, many of the proposed approaches produce unsatisfactory results when exposed to the harsh, uncertain, noisy conditions of a manufacturing environment. In this study, we develop an artificial neural network approach to moderate the effect of the miscellaneous noise sources and harsh factory conditions on the localization of the wireless sensors. Special attention is given to investigate the effect of blockage and ambient conditions on the accuracy of mobile node localization. A simulator, simulating the noisy and dynamic shop conditions of manufacturing environments, is employed to examine the neural network proposed. The neural network performance is also validated through some actual experiments in real-world environment prone to different sources of noise and signal attenuation. The simulation and experimental results demonstrate the effectiveness and accuracy of the proposed methodology.
artificial neural network wireless sensor networks localization ambient conditions simulation signal attenuation mobile node tracking