Measuring Wireless Fingerprints Inside a Wireless Sensor Network

Carleton University, Ontario, Canada. January 2013.

Wireless fingerprints authenticate transmitters in wireless networks, using attributes of the physical wireless signal rather than information being conveyed with that signal. They can be used effectively in an intrusion detection system for networks, where nodes are physically vulnerable. While previous research shows that wireless fingerprints work reliably in a laboratory setting, little work has been published showing the feasibility of their implementation within a real network. We present methods for wireless fingerprints using data sampled at the demodulation rate. This eliminates the need for high bandwidth data processing, making them feasible from inside a wireless sensor network. We analyze their classification performance empirically for different network conditions and theoretically examine aspects of their secure usage in a network.

 

To the best of our knowledge, our research is the only published work that uses representative wireless networking hardware for wireless fingerprints. We discriminate between different IEEE 802.15.4 2.4 GHz Radio Frequency (RF) sources, using the SiLabs IEEE 802.15.4 WSN node development platform and the Ettus Labs USRP1 Software-Defined Radio. The wireless fingerprinting method implemented on the SiLabs WSN node discriminates between RF sources using differences in the Automatic Gain Control circuitry time response during the initial appearance of RF signals. The results show that different RF sources can be distinguished over short transmission distances. The more sophisticated USRP1 device exploits differences in the phase attributes of RF signals using a larger set of demodulated data samples than was available with the WSN node. The USRP1 classifies more accurately over a wider range of network conditions: time, transmission distance and also different receiving devices. Our average classification accuracies are: 99.6% at short range, 95.3% at medium range and 81.9% at long range using five SiLabs devices.

 

We demonstrate the independence of classification errors made over different RF channels and the benefit of using multiple nodes for classification. This suggests that nodes can collaborate to increase the reliability of wireless fingerprints, either by comparing their classification decisions or by aggregating their fingerprints. We present a secure group key establishment protocol, using wireless fingerprints for authentication, for use in a network containing malicious nodes.