Fingerprinting Mobile User Positions in Sensor Networks

We demonstrate that the network flux over the sensor network provides us fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Non-linear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we are able to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. A particular advantage of this approach is that compared to the vast information we can reveal the required knowledge is extremely cheap. As all fingerprint information comes from the network flux that is public under current wireless communication medium, our study indicates that most of existing systems are vulnerable in protecting the privacy of mobile users.