Fingerprints in the Air: Unique Identification of Wireless Devices Using RF RSS Fingerprints

摘要

Recent years have witnessed the proliferation of indoor localization, device identification, and wireless attendance security systems. These solutions typically leverage RF received signal strength fingerprints to locate persons. However, they entail an important albeit commonly ignored issue, i.e., detecting whether an individual is carrying more than one wireless device. In other words, additional devices should be excluded from the analysis. To detect the unique identification problem, bio-assisted methods, such as fingerprint, face, and gait recognition, are deployed near entrances. However, these methods are not only difficult to implement but also entail additional costs. This paper studies the unique identification problem using RF received signal strength fingerprints, which are collected and modeled as time series. The similarity of the time series is calculated to achieve unique identification. Specifically, a naive algorithm based on dynamic time warping is proposed to compute the similarity in the asynchronous time series. Then, an improved two-step algorithm based on feature extraction and spectral clustering is proposed to reduce the computational complexity of the similarity check. In addition, an effectiveness index is proposed to obtain the optimal number of clusters. The results of simulations and experiments show that our algorithms can detect the unique identification problem with moderate computational complexity in typical scenarios.