Using network traffic to infer power levels in wireless sensor nodes

In this paper we leverage the concept of information leakage to demonstrate the correlation between network traffic and available power levels in wireless sensor nodes brought about as a result of dynamic duty cycling. We show that this correlation can be used to remotely infer sensor node power levels. Essentially, our premise is that by determining the send rate of a wireless sensor node the current duty cycle mode and thus available power level of the node can be inferred. Our scheme, namely Power Efficient Path Selection (PEPS), is motivated by the fact that dynamic duty cycling attempts to streamline power usage in wireless sensor nodes by decreasing radio usage, which directly affects the node's network traffic send rate. PEPS is an enhancement to the shortest path algorithm that allows us to 1) reduce the volume of periodic messages since the energy level of neighboring nodes and their statuses are inferred rather than communicated via control packets, and 2) extend the lifetime of a wireless sensor network (WSN) through the selection of energy-aware communication paths. We demonstrate the performance and feasibility of PEPS through simulation and comparative study with the traditional Shortest Path algorithm. The results indicate significant energy savings and the extension of the lifetime of the wireless sensor network when PEPS is employed.