The objective of this project is to evaluate the commercialization potential of a software-defined platform for underwater acoustic communication towards deployment of remote underwater sensing and monitoring networks including the Internet of Underwater Things (IoUT).
The platform is highly integrated and acts as a middleware between the application and physical layer. This platform has two key functions to boost the current underwater acoustic technology.
The first one is a lightweight data encryption on the application layer to protect the end-to-end communication against eavesdropping and data tampering. A lightweight encryption engine software has been designed that encrypts all the user inputs data before assembling and packetizing the user’s bit stream.
This encryption software is compatible with NATO’s JANUS standard for underwater communication and enables establishing a shared secret over an insecure channel. This is achieved by using an efficient Elliptic Curves Diffie-Hellman (ECDH) key exchange to secure peer-to-peer communication links.
Secondly, an integrated signal processing software on a real time embedded processor platform which interfaced to the acoustic transceiver front end, enables the lower layers of communication stack including the physical layer, the routing layer and the media access control (MAC) layer to dynamically adopt to varying channel and topology conditions and transforms the conventional acoustic transceivers to secure and smart network ready nodes.
By processing received beacon signals, the embedded software will acquire knowledge of the links and topology such as the range, depth as well as the acoustic signal direction of arrival (DOA). Then using the processed data estimates, the acoustic transmission loss, propagation delay and channel quality for each link to the neighbor relay nodes will be calculated using the built-in Bellhop software. Using this information, each transmitter node in the network will select its next optimum relay and adjust its channel frequency according to a channel state estimator which is running on a machine learning module. In the routing process, using the ML algorithm, transmitter modem can decide on the best routing path by considering the channels attributes. The routing performance will be evaluated by measuring the packet delivery ratio, latency and energy consumption. This software-defined technology minimizes the number of transmitted control and data packets while maintaining high PDR and low latency by boosting current underwater acoustic modems technology.