Abstract :
Detecting and localizing a person crossing a line segment, i.e., border, is valuable information in security systems and human context awareness. To that end, we propose a border crossing localization system that uses the changes in measured received signal strength (RSS) on links between transceivers deployed linearly along the border. Any single link has a low signal-to-noise ratio because its RSS also varies due to environmental change, (e.g., branches swaying in wind), and sometimes does not change significantly when a person crosses it. The redundant, overlapping nature of the links between many possible pairs of nodes in the network provides an opportunity to mitigate errors. We propose new classifiers to use the redundancy to estimate where a person crosses the border. Specifically, the solution of these classifiers indicates which pair of neighboring nodes the person crosses between. We demonstrate that in many cases, these classifiers provide more robust border crossing localization compared to a classifier that excludes these noisy, redundant measurements.
