Bayesian Landmark Learning for Mobile Robot Localization
Machine Learning
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Data collection, storage, and retrieval with an underwater sensor network
Proceedings of the 3rd international conference on Embedded networked sensor systems
Introduction to Bayesian learning
ACM SIGGRAPH 2004 Course Notes
WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
Acoustic propagation considerations for underwater acoustic communications network development
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
A survey of practical issues in underwater networks
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
Low-power acoustic modem for dense underwater sensor networks
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
MIMO time reversal communications
Proceedings of the second workshop on Underwater networks
Understanding spatio-temporal uncertainty in medium access with ALOHA protocols
Proceedings of the second workshop on Underwater networks
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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.