Fingerprinting channel dynamics in indoor low-power wireless networks

Wireless low-power embedded devices are populating indoor environments, where everyday activities drastically impact communication. We explore a statistical approach to identify changes to the communication links state during system operation. The long-term behavior of the link RSSI is modeled with a normal distribution and compared against the model of the most recent measurements. A Welch's t-Test is then employed to identify whether the short-term and long-term link evolutions stem from the same distribution. Upon significant divergence, the long-term model is updated and a significant change in the underlying communication state is inferred. We investigate this technique to efficiently store a compressed fingerprint of the evolution of communication. Considering the memory constraints of low-power embedded systems, this approach allows to gather extensive information on the behavior of communication directly from the deployed network. This fingerprint could then be used to replay the network dynamics in simulation. We implemented the introduced techniques to prove their feasibility. In controlled experiments, we evaluate the reactivity and sensitivity of the approach to changes in the environment, as well as the accuracy of the resulting channel fingerprint.

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