A slotted CSMA based reinforcement learning approach for extending the lifetime of underwater acoustic wireless sensor networks

In this work, we present an energy efficient architecture for UA-WSNs, which employs a reinforcement learning algorithm and a slotted Carrier Sensing Multiple Access (slotted CSMA) protocol. Due to the reinforcement learning algorithm, the proposed system is capable of optimising its parameters to adapt to the underwater environment after having been deployed. Simulation results show that the lifetime of the network is extended significantly with the proposed architecture by lowering the number of collisions and retransmissions of data packets.