Tones for Real: Managing Multipath in Underwater Acoustic Wakeup
The principles of sensor networks-low-power, wireless, in-situ sensing with many inexpensive sensors-are only recently penetrating into underwater research. Acoustic communication is best suited for underwater communication, with much lower attenuation than RF, but acoustic propagation is five orders-of-magnitude slower than RF, so propagation times stretch to hundreds of milliseconds. Low-power wakeup tones are present in new underwater acoustic modems, and when added to applications and MAC protocols they reduce energy consumption wasted on idle listening. Unfortunately, underwater acoustic tones suffer from self-multipath-echoes unique to the latency that can completely defeat their protocol advantages. We introduce Self-Reflection Tone Learning (SRTL), a novel approach where nodes use Bayesian techniques to address interference by learning to discriminate self-reflections from noise and independent communication. We present detailed experiments using an acoustic modem in controlled and uncontrolled, in-air and underwater environments. These experiments demonstrate that SRTL's knowledge corresponds to physical-world predictions, that it can cope with underwater noise and reasonable levels of artificial noise, and that it can track a changing multipath environment. Simulations confirm that these real-world experiments generalize over a wide range of conditions.
algorithms experimentation acoustic networks underwater sensor networks bayesian learning sensor network