Underwater Acoustic Sensor Networks (UASNs) have attracted much attention due to various aquatic applications. However, UASNs have many specific characteristics, such as high propagation delay, high packet error rate and low bandwidth, which bring challenges to network congestion control. Furthermore, the point-to-point congestion control algorithms cannot guarantee the end-to-end optimal performance. Therefore, congestion avoidance is an important issue to be considered when designing routing protocols for UASNs. In addition, since the sensor nodes deployed underwater are powered by batteries, which are hard to be replaced or recharged, energy limitation should be taken into account as well. In this paper, we propose a reinforcement-learning based congestion-avoided routing (RCAR) protocol to reduce the end-to-end delay and energy consumption. With the application of reinforcement learning, the protocol converges to the optimal route from the source node to the surface sink by exploring hop by hop. In RCAR, a reward function in reinforcement learning is defined in which congestion and energy are both considered for adequate routing decision. To accelerate the convergence of the algorithm, we introduce a dynamic virtual routing pipe with variable radius, which is related to the average residual energy of the neighbors of the sender node. Moreover, in the cross-layer-information based RCAR protocol, an information update method based on handshake in the MAC layer is proposed, which guarantees the optimal routing decision. The simulation results show that the proposed RCAR protocol outperforms HHVBF, QELAR and GEDAR in terms of convergence speed, energy efficiency and end-to-end delay.

The accurate localization of nodes is one of the most basic tasks in Underwater Acoustic Sensor Networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received, and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this paper, we propose a Mobile-beacon Based Iterative Localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multi-hop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism firstly uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network’s lifetime.

Underwater sensor networks (UWSNs) have become a hot research topic because of their various aquatic applications. As the underwater sensor nodes are powered by built-in batteries which are difficult to replace, extending the network lifetime is a most urgent need. Due to the low and variable transmission speed of sound, the design of reliable routing algorithms for UWSNs is challenging. In this paper, we propose a Q-learning based delay-aware routing (QDAR) algorithm to extend the lifetime of underwater sensor networks. In QDAR, a data collection phase is designed to adapt to the dynamic environment. With the application of the Q-learning technique, QDAR can determine a global optimal next hop rather than a greedy one. We define an action-utility function in which residual energy and propagation delay are both considered for adequate routing decisions. Thus, the QDAR algorithm can extend the network lifetime by uniformly distributing the residual energy and provide lower end-to-end delay. The simulation results show that our protocol can yield nearly the same network lifetime, and can reduce the end-to-end delay by 20–25% compared with a classic lifetime-extended routing protocol (QELAR).

Efficient routing protocols for data packet delivery are crucial to underwater sensor networks (UWSNs). However, communication in UWSNs is a challenging task because of the characteristics of the acoustic channel. Network coding is a promising technique for efficient data packet delivery thanks to the broadcast nature of acoustic channels and the relatively high computation capabilities of the sensor nodes. In this work, we present GPNC, a novel geographic routing protocol for UWSNs that incorporates partial network coding to encode data packets and uses sensor nodes’ location information to greedily forward data packets to sink nodes. GPNC can effectively reduce network delays and retransmissions of redundant packets causing additional network energy consumption. Simulation results show that GPNC can significantly improve network throughput and packet delivery ratio, while reducing energy consumption and network latency when compared with other routing protocols.