ECRKQ: Machine Learning-Based Energy-Efficient Clustering and Cooperative Routing for Mobile Underwater Acoustic Sensor Networks

The dynamic topology, narrow transmission bandwidth, and limited energy of sensor nodes in mobile underwater acoustic sensor networks (UASNs) pose challenges to design an efficient and robust network for underwater communications. In this paper, we propose a novel machine learning-based clustering and routing scheme, named energy-efficient clustering and cooperative routing based on improved K-means and Q-learning (ECRKQ), to reduce and balance energy consumption among sensor nodes in a mobile UASN and improve the bandwidth utilization. In the cluster head (CH) selection stage, ECRKQ modifies the K-means algorithm to dynamically select a CH based on the residual energy of the node and the distance from the node to the centroid in a cluster. In the clustering stage, ECRKQ adopts the Q-learning algorithm by incorporating the residual energy of the CH, the energy consumption of data transmission from the node to the CH, and the energy consumption of the data transmission from the CH to the base station into the Q-value function. In the data transmission stage, ECRKQ applies the dynamic coded cooperation (DCC) transmission to improve the bandwidth utilization and the robustness of the underwater communications. In the DCC transmission, cooperative nodes are also dynamically selected based on the residual energy and the energy consumption of transmitting a packet to their destinations. In the simulation, we apply the ocean current drifting model to emulate the position variation of nodes caused by ocean currents in a mobile UASN. The simulation results show that the proposed ECRKQ scheme can achieve more balanced energy consumption among sensor nodes in a mobile UASN than that of the existing scheme.