Design of a Decentralized AI IoT System Based on Back Propagation Neural Network Model

In the Internet of Things (IoT) era, when user needs are continually evolving, the coupling of AI and IoT technologies is unavoidable. Fog devices are introduced into the IoT system and given the function of hidden layer neurons of Back Propagation neural network, and Docker containers are combined to realize the mapping of devices and neurons in order to improve the quality of service of IoT devices. This study proposes the design of a decentralized AI IoT system based on Back Propagation neural network model. The testing data revealed that, at various data transfer intervals, the average transmission rate between the fog device and the sensing device was 8.265Mbps, and that the device's transmission rate could satisfy user demand. When the data transmission interval was 20s, the network data transmission rate was greater than 8.5Mbps and did not vary much when the number of data transmissions rose. The research demonstrates that the decentralized AI IoT system's network performance, which is based on a back propagation neural network model, can match user usage requirements and has good stability.

@article{Zhang2023,
  title     = {Design of a Decentralized AI IoT System Based on Back Propagation Neural Network Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Xiaomei Zhang},
  doi       = {10.14569/IJACSA.2023.0140899},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140899}
}