Cross-layer Design for Software-defined Underwater Acoustic Networking

In this paper, a network relying on software-defined underwater acoustic nodes is proposed: by receiving a periodic beacon signal from the sink node, each node in a cluster obtains a prediction about the communication quality of the potential links to its one-hop neighbors as well as to the available relay nodes within the cluster. A hidden Markov process is used to predict the next state of the channels, using the probability distribution of the channel amplitude and delay spread from previous observations. Each transmitting node evaluates the quality of links to its next hop neighbor relay nodes. Accordingly, a normalized weight representing the channel quality is assigned to each link. The channel gain and its delay spread are the two metrics that are used to define the channel quality. To evaluate the network performance in realistic conditions, the output of a statistical model combined with the Bellhop ray tracing software is compared to experimental data. Using the channel quality, each transmitter node in the network selects its next hop optimum relay node. This scheme minimizes the number of transmitted control packets and also reduces the re-transmission of data packets, by predicting the channel status rather than exchanging an excessive number of control packets which typically convey expired channel state information. Minimizing the overhead and selecting the optimum channel saves on the energy per bit consumption while maintaining high packet delivery ratio and low latency.