We discriminate between different SiLabs IEEE 802.15.4 2.4GHz RF sources using the Ettus Labs USRP1 Software-Defined Radio. The wireless fingerprinting method implemented on the USRP1 device exploits differences in the phase attributes of demodulated data samples. The method does not require the use of expensive spectrum analyzer equipment and the associated high sampling and processing rates with such equipment. Instead, data sample inputs are used, sampled at a rate of 4MHz. This makes implementation using real Wireless Sensor Network nodes feasible and allows wireless fingerprintstobe gathered inside each node in a network. This is important since wireless fingerprints degrade over distance, making distributed implementations more attractive. With our method, the USRP1 classifies accurately over a wide range of network conditions, including time and transmission distance. Performance is also stable for different receiving devices. We achieve average classification accuracies of 99.6% at short range, 95.3% at medium range, and 81.9% at long range when classifying a limited sample of five devices from the same manufacturer.

Additional Metadata
Keywords Software-Defined Radio, Wireless fingerprints
Persistent URL dx.doi.org/10.1145/2658999
Journal ACM Transactions on Sensor Networks
Citation
Knox, D.A., & Kunz, T. (2015). Wireless fingerprints inside a Wireless Sensor Network. ACM Transactions on Sensor Networks, 11(2). doi:10.1145/2658999