Abstract
Aiming at the low efficiency and high power consumption of data acquisition in the Internet of Things, a data acquisition system based on the combination of neural network and STM32 microcontroller is designed. In the control module, according to the system functional design and performance requirements, the STM32F103C8T6 minimum system module is used as the control core of the system. The data acquisition terminal adopts the baseplate + core board structure, and is composed of high-speed data acquisition module, AT91SAM9X25 platform and various expansion function modules. Eight types of environmental data sensor modules are used to build sensor modules. The k-means convolution neural network is used to realize the data classification of the Internet of Things, and the design of the data classification module is completed. The performance of the system is tested. The test results show that the system can realize the real-time collection of indoor environmental data, the communication rate is higher than 80 mbps, and the power consumption of the data acquisition terminal is low.
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References
Wang, Y., Gao, Y.: State prediction simulation of IoT terminal secure access data gateway. Comput. Simul. 39(4), 341–344, 356 (2022)
Yan, G., Huang, X., Haiwu, X.: Design of a wireless sensing data acquisition system for NB-IoT. Modern Electron. Technol. 44(18), 38–42 (2021)
Jia, J., He, Y., Hong, Y.: Design of multi-channel data acquisition system based on Internet of Things. Ind. Instrum. Autom. 4, 21–24 (2020)
Wang, M., Liu, W., Xu, Y., et al.: Single-chip hybrid integrated silicon photonics transmitter based on passive alignment. Opt. Lett. 47(7), 1709–1712 (2022)
Vinjamuri, U.R., Rao, B.L.: Efficient energy management system using Internet of things with FORDF technique for distribution system. IET Renew. Power Gener. 15(3), 676–688 (2021)
Park, J., Kim, S., Youn, J., et al.: Low-complexity data collection scheme for UAV sink nodes in cellular IoT networks. IEEE Trans. Veh. Technol. (99), 1 (2021)
Sun, Z., Wei, Z., Yang, N., et al.: Two-tier communication for UAV-enabled massive IoT systems: performance analysis and joint design of trajectory and resource allocation. IEEE J. Sel. Areas Commun. (99), 1 (2020)
Sun, Z., Zhang, X., Wang, T., et al.: Edge computing in internet of things: a novel sensing-data reconstruction algorithm under intelligent-migratoin stragegy. IEEE Access, (99), 1 (2020)
Kim, T., Qiao, D.: Energy-efficient data collection for IoT networks via cooperative multi-hop UAV networks. IEEE Trans. Veh. Technol. 69(11), 13796–13811 (2020)
Fazlollahtabar, H.: Internet of Things-based SCADA system for configuring/reconfiguring an autonomous assembly process. Robotica 40(3), 672–689 (2022)
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Chen, X. (2024). Design of Iot Data Acquisition System Based on Neural Network Combined with STM32 Microcontroller. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-031-50577-5_4
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DOI: https://doi.org/10.1007/978-3-031-50577-5_4
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