article · IEEE Transactions on Vehicular Technology
Indoor positioning based on received signal strength indicators often suffers from signal instability, noise, and susceptibility to cyber-attacks, which can increase operational costs. To address this, an Internet of Things framework has been designed to locate automated guided vehicles using a deep convolutional neural network. The technique transforms one-dimensional signal data into two-dimensional images through continuous wavelet transforms, generating enriched spatial features for the neural network to process. Test results indicate that this model achieves smaller positioning errors than other current techniques. Additionally, the approach demonstrates high resilience against multiple forms of adversarial attacks while significantly dampening the negative impacts caused by signal strength variations during indoor vehicle navigation.
Reliable indoor positioning is crucial for operating automated guided vehicles safely within Internet of Things environments. Wireless signal instability and cyber-attacks often degrade navigation accuracy or compromise system integrity. By translating fluctuating signals into structured image data and defending against adversarial threats, this method improves the reliability and security of automated industrial transport.
This technology could enable safer, more precise indoor navigation for automated guided vehicles used in factories, warehouses, and logistics networks. The primary users are developers and operators of industrial Internet of Things systems. Based on the abstract, the method has been developed, tested, and validated against attacks, placing it at an applied and tested research stage rather than ready for immediate commercial deployment.
AI-generated from the published abstract. Always read the original work before citing.
The effectiveness of positioning techniques that utilize the receiver signal strength (RSS) is highly dependent on the instability of the received signal strength indicator (RSSI). Up to now, there is no strategy that effectively lowers the influence of such instability on the accuracy of positioning. Moreover, recent studies showed that indoor positioning techniques are vulnerable to noise in RSSI data and cyber-attacks, which make them more expensive. In this study, a new internet of things (IoT) paradigm is proposed for the indoor positioning of automated guided vehicles (AGVs) using a deep convolution neural network (CNN). The proposed method handles signal processing by converting the RSSI signal into an image. In which, the 1-dimensional RSSI signal is converted into 2-dimensional image data in order to generate the new features based on continuous wavelet transform (CWT), and then the proposed deep CNN is implemented for the indoor positioning system. The test results show that the proposed model can outperform other state-of-the-art positioning techniques with small position errors. Furthermore, the robustness of the proposed model is validated against various adversarial attacks. In addition, the proposed method can have a lower impact on RSSI change compared with other methods
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DOI: 10.1109/tvt.2024.3357780
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