Research in underwater communication is rapidly becoming attractive due to its various modern applications. An efficient mechanism to secure such communication is via physical layer security. In this paper, we propose a novel physical layer authentication (PLA) mechanism in underwater acoustic communication networks where we exploit the position/location of the transmitter nodes to achieve authentication. We perform transmitter position estimation from the received signals at reference nodes deployed at fixed positions in a predefined underwater region. We use time of arrival (ToA) estimation and derive the distribution of inherent uncertainty in the estimation. Next, we perform binary hypothesis testing on the estimated position to decide whether the transmitter node is legitimate or malicious. We then provide closed-form expressions of false alarm rate and missed detection rate resulted from binary hypothesis testing. We validate our proposal via simulation results, which demonstrate errors’ behavior against the link quality, malicious node location, and receiver operating characteristic (ROC) curves. We also compare our results with the performance of previously proposed fingerprint mechanisms for PLA in underwater acoustic communication networks, for which we show a clear advantage of using the position as a fingerprint in PLA.

When an underwater acoustic sensor network (UASN) is applied to underwater data collection, different data importance rating (DIR) of sensor nodes will affect the scheduling time slot of data collection. In this paper, we propose a Q-learning and DIRbased media access control (Q-DIR MAC) protocol for dynamic clustering underwater acoustic sensor networks (UASNs), in which the nodes in the network may drift with the movement of ocean currents. We use k-mean algorithm to divide the nodes into several clusters. Each partitioned cluster is composed of one cluster head (CH) and several cluster members (CMs). The CMs can be divided into three levels according to the DIR: non-urgent, normal, and very urgent. The number of three types of nodes follows normal distribution. The data importance of each node is introduced into reward function design of Q-learning. The results show that, in the dynamic clustering UASNs, the proposed QDIR MAC protocol can ensure that important data can be sent to the destination node in time without reducing the data success rate under the condition of priority transmission mechanism.

Propagation delay and channel loss are two vital factors affecting reliability of Underwater Acoustic Networks (UANs). Different from land networks, UANs have long propagation delay and poor channel quality, which lead to serious data collision and high bit error rate, respectively. However, complex underwater environments impose great challenges to evaluate propagation delay and channel loss. As temperature is the most critical factor affecting them, in this paper, we propose to employ temperature to evaluate them. However, existing temperature prediction research are insufficient for accuracy or efficiency. This paper proposes a temperature prediction-assisted approach for evaluating propagation delay and channel loss, aiming to improve reliability and performance of underwater acoustic networks. We build a nonlinear autoregressive dynamic neural network-based temperature prediction model to improve prediction accuracy and reduce time complexity. Then, we evaluate propagation delay and channel loss considering different marine environments, including shallow and deep sea. Extensive simulation results show that our approach performs better than five advanced baselines.

With the rapid development of underwater sensor networks, the design of underwater demodulators become increasingly significant. However, underwater acoustic communication is faced with many problems such as propagation time delay, multipath effect and Doppler effect due to the complexity of underwater environment. Demodulation of underwater communication signals is a challenging task. To solve this problem, we propose a novel binary phase shift keying (BPSK) demodulator for underwater acoustic communication based on convolutional neural network, which demodulates the modulation data by detecting the position of phase shift. The method proposed in this paper significantly reduces the bit error rate (BER) compared with the results of the traditional method in URPC1 datasets (Underwater Robot Picking Contest).

Underwater acoustic sensor network (UASN) is a promising underwater networking technology for wide applications, but there is an urgent need to design reliable and low power consumption routing protocols for UASN to extend network lifetime due to the limited energy supply. In this paper, we propose a Q-learning and data priority-based routing protocol with dynamic computing cluster head (QD-DCR) to extend the network lifetime of UASN. In QD-DCR protocol, the underwater nodes are clustered and set the cluster head (CH) nodes, which are only responsible for computing the optimal path of data transmission and the storage of Q-value table, while the non-CH nodes are responsible for data transmission. Meanwhile, according to the data priority, we design different data transmission methods that can effectively use the limited resources of UASN to transmit urgent data. To further make the residual energy of sensor nodes evenly distributed, we also design the dynamic selection of CH node, which can avoid the potential energy holes. In addition, we adopt Q-learning to determine the optimal next hop instead of the greedy next hop in a cluster. We also define an action utility function that takes into account both residual energy and node depth to extend the network lifetime by distributing the residual energy evenly. Simulation results show that the proposed QD-DCR protocol can effectively extend the network lifetime compared with a classic lifetime-extended routing protocol (QELAR), while alleviating the issue of uneven distribution of the residual energy in the network.

Underwater Acoustic Sensor Networks (UASNs) is a prominent field in communications due to several applications. UASNs enable underwater data collection and monitoring in different applications. UASNs face several challenges like node mobility, low bandwidth, high energy consumption, and routing. The complexity of the underwater routing is increased due to node mobility. Several underwater routing protocols exist in the literature; they determine next-hop based on different criteria. Some criteria to select next-hop are link quality, residual energy, hop-count, etc. Many underwater routing protocols either use hop-count or hop-count as one of the criteria to choose nexthop. Such routing protocols result in lower hop-count, resulting in smaller end-to-end delays. These routing protocols are instrumental in the delay-sensitive applications where the endto-end delay is the primary requirement. However, maintaining up-to-date information of the hop-count of nodes is one of the major challenges due to frequent changes in underwater topology caused due to the water current. This survey paper focuses on underwater routing protocols based on hop-count in selecting the next-hop. It focuses on updating hop-count information in various hop-count-based underwater routing protocols.

Acoustic communication is a key enabler for underwater Internet of Things networks between autonomous underwater platforms. Underwater Internet of Things networks face a harsh communications environment and limited energy resources which makes them susceptible to interference, whether intentional (i.e., jamming) or unintentional. Resilient, power efficient waveforms and modulation schemes are needed for underwater acoustic communications in order to avoid outages and excessive power drain. We explore the impact of modulation scheme on the resiliency of underwater acoustic communications in the presence of channel impairments, interference, and jamming. In particular, we consider BFSK and OFDM schemes for underwater acoustic communications and assess the utility of Polar coding for strengthening resiliency.