A scalable analytical framework for deriving optimum scheduling and routing in underwater sensor networks

Underwater sensor networks have become an important area of research with many potential practical applications. Given impairments of optical and radio propagation, acoustic communication is used for underwater networking, which translates into variable and long propagation delays, low data rates, long interference ranges and significant fluctuations in terms of link quality over time. A complete characterization of the unique features of the acoustic channel introduces significant complexity both in analytical models and in simulators but is needed for correct characterization of underwater protocols performance. Our objective has been that of designing scalable analytical techniques which are able to derive optimum traffic scheduling and routing for underwater sensor networks while accurately capturing underwater channels features. Specifically the paper presents an analytical model for joint MAC and routing optimization which produces the optimum solution for small to medium scale underwater networks. Scalable, centralized heuristics are then designed, which combine approximate analytical models and scheduling heuristics, and are able to generate solutions close to the optimum. The overall result is a powerful tool to derive benchmark results (upper bounds) for underwater protocol performance and to understand the tradeoffs and performance limits of such systems