Abstract
The Internet of Things (IoT) aims to meet the needs of smart services by combining multiple information intelligence tools and network technologies. In terms of technology, management, cost, policy, and security, the development of the IoT still faces many challenges. This study proposes an intelligent image detection and transmission system based on IoT communication, with the following primary objectives: First, this study aims to provide an early warning model based on Kalman Filtering Fast Fourier Transform Support Vector Machine (KF-FFT-SVM). It delivers early warning signals by analyzing and extracting historical features from surface motion data for a given period. The step size for spectrum analysis is determined by the signal frequency and is used to create a training dataset and train the SVM model. The use of a trained model for early warning can improve the accuracy of early warning evaluation. Second, the forward line of the video image is used as the necessary information in the content symbol retrieval process, and the information required for the structure is used to improve the quality as much as possible. Because of the search and influence of the transmission quality of digital components, the important data in the digital transmission space is used to ensure the accuracy of digital components while transmitting a small amount of energy. Third, when the device is connected to the network, other users can obtain information about the device via security breaches. The data must be transmitted by both parties using their own identities, which increases transmission security. The design idea of ZigBee technology follows the method of distributing information, such as food space through a zigzag-shaped dance, and low power consumption with low cost. Finally, to increase system efficiency, non-orthogonal multiple access technologies realize and transmit data from multiple users in time, frequency, and code zones while using various channels for each user. The wireless signal-enabled environment is changed by varying the reflection coefficient of the passive reflector units, which are components of the same mapping set as the smart reflector. To improve the effectiveness and performance of the system transmission, the wireless signal may have the effect of boosting the signal or removing interference.
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Data Availability
The experimental data used to support the findings of this study are available from the corresponding author upon request.
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Acknowledgements
The research in this paper is supported by West Anhui University’s Natural Key Project of “Research on Bud Detection and Disease Identification of Lu’an Gua Pian (Tea) based on Convolutional Neural Network” (NO. WXZR202017)
Funding
This work was sponsored in part by Wiest Anhui University’s Natural Key Project of “Research on Bud Detection and Disease Identification of Lu’an Gua Pian (Tea) based on Convolutional Neural Network” (NO. WXZR202017).
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Zhang, J. Designing an intelligent image detection and transmission system for the Internet of Things. Wireless Netw 29, 1213–1222 (2023). https://doi.org/10.1007/s11276-022-03121-7
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DOI: https://doi.org/10.1007/s11276-022-03121-7