Leveraging Participatory Extraction to Mobility Sensing for Individual Discovery in Crowded Environments

Abstract EN

Neighbor discovery for moving individual is considered an important technology submitting to location-based service (LBS), which includes such things as recruitment flow of information, logical localization, and health monitoring.

Based on the tradeoff between universality and accuracy of neighbor discovery, we propose the environmental characteristics participatory extraction method benefiting to mobile individual discovery.

First, we fuse lightweight accelerometer, light sensors, and microphone collaboratively.

Furthermore, support vector machine (SVM), Tanimoto coefficient, and Manhattan distance are used to calculate three kinds of fingerprint similarity, respectively, and then the principal component analysis based method reduces data dimension in order to obtain neighbor similarity rank.

Finally, the experiment data are collected by 25 volunteers, and trace-driven simulations show that Euclidean distance error is below 4.69 and the convergence time is within 0.75 s.