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).

Because of the characteristics of the underwater acoustic sensor networks (UASNs), such as long propagation delay, narrow bandwidth, limited resources, and intermittent connectivity, the design of the routing protocol has always been the focus and difficulty in UASNs research. Most of the existing coding-based underwater routing protocols only use the error correction function of network coding to improve the reliability of data transmission, ignoring that the network coding can effectively optimize the performance of packet transmission. In this article, we propose a hybrid coding-aware routing protocol (HCAR). HCAR introduces interflow network coding and designs a new routing framework that combines reactive routing with opportunistic routing. It exhaustively explores potential coding opportunities in the network and proactively creates coding opportunities during the entire routing process, which enables HCAR to take full advantage of the broadcast nature of the wireless medium, thereby effectively reducing the number of transmissions. In addition, we design a new encoding method that does not rely on opportunistic listening, and thus avoids introducing excessive delay and overhead. The simulation results show that HCAR can effectively optimize the routing problem of the networks with multiple source nodes and a single sink node, further reducing the transmission overhead while ensuring reliability. We also implemented the key mechanism of the HCAR protocol to verify its feasibility through sea tests.

Underwater acoustic sensor networks (UASNs) have gradually received attention due to their widespread applications, such as in disaster prevention, environmental monitoring and military activities, and they simultaneously face security challenges. In recent years, trust management mechanisms have become important tools for responding to internal attackers. However, most of the existing trust management mechanisms do not consider the adverse influence of complex underwater environment on node evaluation, and do not deal with the case that normal nodes are misjudged as malicious nodes. To achieve accurate trust evaluation of nodes and reduce the possibility of misjudgment of normal nodes, a redeemable Support Vector Machine-Dempster-Shafer (SVM-DS) fusion-based trust management mechanism (SDFTM) is proposed for UASNs in this paper. First, according to the characteristics of attacks, three kinds of trust evidence are selected: packet-based evidence, data-based evidence and energy-based evidence. Then the support vector machine (SVM) is applied to classify the trust of node from the aspect of each kind of trust evidence, while Dempster-Shafer (DS) evidence theory is used to fuse the different trust classification results of nodes. Second, to deal with cases where a normal node may be misclassified as a malicious node, trust redemption process is carried out based on the historical performance and environmental influence (unreliable acoustic channel, weak link connectivity). Finally, the trust value is calculated and updated. The simulation results prove that the proposed scheme yields satisfactory performance in malicious detection and reduce the possibility of misjudgment of normal nodes.