Login / Signup

An Enhanced Food Digestion Algorithm for Mobile Sensor Localization.

Shu-Chuan ChuZhi-Yuan ShaoNing ZhongGeng-Geng LiuJeng-Shyang Pan
Published in: Sensors (Basel, Switzerland) (2023)
Mobile sensors can extend the range of monitoring and overcome static sensors' limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results.
Keyphrases
  • machine learning
  • deep learning
  • patient safety
  • risk assessment
  • neural network
  • human health
  • quality improvement
  • electronic health record