An Enhanced Food Digestion Algorithm for Mobile Sensor Localization

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.

Automated summary

Mobile sensors are being used more frequently in real-life applications as they can extend monitoring range and overcome limitations of static sensors. This paper improves the Monte Carlo Localization (MCL) algorithm by enhancing the food digestion algorithm (FDA) used in the localization of mobile sensors to reduce errors and improve accuracy. The paper proposes three inter-group communication strategies based on the topology between groups to accelerate the convergence of the algorithm, and the improved algorithm achieves good localization results.