With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal st...
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With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal strength (RSS) and channel state information (CSI) in railway communications are measured by AR9344 network interface card. Then, based on the measurement data, the authors propose trajectory-based, neural network-based (NN-based) and ray tracing-based (rt-based) localisationalgorithms, serving for location-based security access control. Specifically, the trajectory-basedalgorithm combined with trajectory simulation, movement detection and dynamic time warping algorithms, realises passengers enter/exit pattern detection. The NN-basedalgorithm leverages back-propagation network (BPN) and constructs training sets with measurement-based RSS and CSI, finishing accurate localisation. Besides, they evaluate the algorithm performance under different layers of BPN. rt-based localisation algorithm combines measurement data and simulation analysis, leveraging simulated-based multiple-input multiple-output received power and delay spread to realise lightweight localisation. After evaluation, the rt-basedalgorithm can achieve the highest accuracy of localisation, up to 99.9% and is designed to be straightforward for integration with commercial access points and deployment to railway communications.
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