Data Collection Method Based on Data Perception and Positioning Technology in the Context of Artificial Intelligence and the Internet of Things

Wireless sensor networks are an important technical form of the underlying network of the Internet of Things. The energy of each node in the network is finite. When a node runs out of energy, it can cause network interruptions, which can affect the reliability of data collection. To reduce the consumption of communication resources and ensure the reliability of data collection, the study proposes data collection based on data compression perception positioning technology. This method first uses a Bayesian compression perception method to select nodes, and then adopts an adaptive sparse strategy to collect data. When selecting nodes using this proposed method, wireless sensor networks had the longest network lifespan. In the case of different degrees of redundancy and sparsity, the research method had the lowest reconstruction error, with reconstruction errors of 0.31 and 0.40, respectively. When the balance factor was set to 0.6, the reconstruction error of the research method was the lowest, with a minimum reconstruction error of 0.05. This proposed method has better reconstruction performance, effectively prolongs the lifespan of wireless sensor networks, and reduces the consumption of communication resources.

@article{Zhao2024,
  title     = {Data Collection Method Based on Data Perception and Positioning Technology in the Context of Artificial Intelligence and the Internet of Things},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xinbo Zhao and Fei Fei},
  doi       = {10.14569/IJACSA.2024.01508109},
  url       = {https://doi.org/10.14569/IJACSA.2024.01508109}
}