QL-EEBDG: QLearning based energy balanced routing in underwater sensor networks

In this paper, we propose a Q-Learning based efficient and balanced energy consumption data gathering routing protocol (QLEEBDG) for underwater sensor networks (USNs). We set an optimal next hop forwarder for each node to transmit its the sensed data. This helps to reduce distance between sender and receiver. The energy consumption is minimum. Furthermore, a node is considered an eligible forwarder node only if its next hop neighbour exists. We incorporate this mechanism to avoid void hole problem. Our technique minimizes energy consumption in the network, hence, lifespan increases. The performance of our proposed technique is validated through extensive simulations.