Understanding and exploiting the acoustic propagation delay in underwater sensor networks.

摘要

An understanding of the key areas of difference in acoustic underwater sensor networks and their impact on network design is essential for a rapid deployment of aquatic sensornets. Such an understanding will allow system designers to harvest the vast literature of research present in RF sensornets and focus on just those key aspects that are different for acoustic sensornets. Most complexities at the physical layer will eventually be handled either by assuming short ranges or with technology advancements making complex algorithms both cost and power efficient. However, the impact of large latency and the resulting magnification of multipath will remain a great impediment for developing robust sensor networks. This thesis contributes towards an understanding of, and solutions to, the impact of latency on sensornet migration to an underwater acoustic environment.;The thesis of this dissertation is that Latency-awareness allows both migration of existing terrestrial sensornet protocols and design of new underwater protocols that can overcome and exploit the large propagation delay inherent to acoustic underwater networks. We present four studies that contribute to this thesis. First, we formalize the impact of large propagation delay on networking protocols in the concept of space-time uncertainty. Second, we use the understanding developed from this concept to design the first high-latency aware time synchronization protocol for acoustic sensor networks that is able to overcome an error source unique to the underwater environment. Third, we exploit the space-time volume during medium access to propose T-Lohi, a new class of energy and throughput efficient medium access control (MAC) protocols. Last, with our protocol implementations we are able to indicate the importance of a different type of multipath which we call self-multipath. This self-multipath adversely affects the throughput of T-Lohi MAC, and to overcome this affect we develop a novel Bayesian learning algorithm that can learn-and-ignore such multipath.