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Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms.

Underwater acoustic communication plays an important role in underwater wireless sensor network. Different from terrestrial radio channel, modeling of underwater acoustic channel is very challenging due to its unique and dynamic characteristics. In order to verify the proposed acoustic communication algorithms, a general emulation platform which can reduce the verification cost has a wide application prospect. In this paper, we design a general hardware in the loop underwater communication emulation system based on the LabVIEW and MATLAB software and real acoustic transducer. In the emulation system, LabVIEW calls MATLAB script to achieve algorithm implementation, and then uses DAQmx to send and receive data through the acquisition card. The proposed system supports a variety of modulation and demodulation methods, such as multiple frequency-shift-keying(MFSK), multi-carrier frequency-shift-keying(MCFSK), orthogonal frequency division multiplexing(OFDM), etc. In addition, in order to detect frame efficiently, we propose a light-weight frame synchronous algorithm. To verify the function of the systems, lake test and sea test are conducted for different algorithms.

Underwater acoustic channels are characterized by long propagation delay, limited available bandwidth and high energy costs. These unique characteristics bring challenges to design media access control (MAC) protocol for underwater acoustic sensor networks (UASNs). Especially in data-collection-oriented UASNs, data generated at underwater nodes are transmitted hop-by-hop to the sink node. The traffic loads undertaken by nodes of different depths are different. However, most existing MAC protocols do not consider the traffic load imbalance in data-collection-oriented UASNs, resulting in unfairness in how the nodes transmit their own generated data. In this paper, we propose a novel traffic-aware fair MAC protocol for layered data-collection-oriented UASNs, called TF-MAC. TF-MAC accesses a medium by assigning time slots of different lengths to each layer via different traffic loads to achieve traffic fairness of nodes. To improve throughput and avoid collision in the network, an overlapping time slot division mechanism for different layers and multi-channel allocation scheme within each single layer is employed. Considering the time-varying traffic loads of the nodes, an adaptive packet length algorithm is proposed by taking advantage of the spatial-temporal uncertainty of underwater channels. A sea experiment was conducted to prove the spatial-temporal uncertainty of UASNs, which provides a feasibility basis for the proposed algorithm. Simulation results show that TF-MAC can greatly improve the network performance in terms of throughput, delay, energy consumption, and fairness in the layered data-collection-oriented UASNs.

In recent years, marine exploration has become one of the most popular research subjects. At present, gliders in Underwater Acoustic Sensor Networks (UASNs) are equipped with a variety of sensors, which can play an important role in marine detection and monitoring. In addition, gliders also have the ability of data collection and storage. When coexisting with the sensor nodes, they can be regarded as special sensor nodes with semi-determined sawtooth trajectories. In hybrid UASNs including sensor nodes and gliders, a robust routing protocol is required to improve the poor network connectivity caused by long transmission delay, high bit error rate and unreliable transmission links. In this article, the Fuzzy Logic Algorithm (FLA) is used to convert multiple input parameters into one output value, thus reducing the storage burden of the network. Therefore, a glider-assist routing scheme with trajectory prediction is proposed to improve the connectivity of the hybrid network. The simulation results show that our scheme is superior to other protocols in terms of delivery ratio, end-to-end latency and lifetime.

Different from the mechanism of medium access control (MAC) for the land-based sensor networks, the underwater acoustic sensor network faces problems such as high propagation delay, spatial-temporal uncertainty and high energy consumption. Orthogonal frequency division multiple access (OFDMA) is a channel access mechanism based on OFDM multi-carriers modulation technology. In OFDMA, multiple users can make use of the orthogonality of the subchannels to simultaneously access channel for parallel transmission. For underwater multi-hop network scenario, this paper proposes a MAC protocol of the joint relay selection and power allocation (JRSPA-MAC) based on OFDMA mechanism. The proposed protocol is divided into flowing phases: Firstly, the node determines the hop distance from the sink nodes and sends the request when there are packets to be sent. Then forwarding nodes election algorithm is proposed based on the channel condition and the delay information. On this basis, the sending nodes autonomously use the particle swarm optimization (PSO) algorithm to allocate its subchannels' power. Secondly, taking advantage of the high underwater acoustic propagation delay, the receiving node reasonably schedule the sending time, makes sure the parallel communication of multiple pairs of nodes in one handshake period. Finally, the destination nodes and the forwarding nodes acknowledge the received packets and completes this data transmission. The simulation results show that compared with the hand-shake based MAC protocols, the JRSPA-MAC reduces the probability of packet loss caused by data conflicts between nodes. In case of low power consumption, the network throughput are effectively improved, and the average end-to-end delay of the network is reduced.

Research challenges have focused on underwater acoustic sensor networks characterized by long propagation delay, narrow bandwidth, and frequently disconnected interruption, among others, which can be viewed as delay/disruption tolerant networks (DTNs). However, limited research has been conducted on goodput performance considering the total data packets transmitted in the channel, which is referred to as the remove redundant goodput (RRG) performance of underwater DTN sensor networks. In this paper, we propose a transport layer protocol, called optimal retransmission timeout (RTO) interval stop-and-wait transmission (ORIT), used in underwater DTN sensor networks. In most general transport layer protocols, the RTO timer is set to longer than the RTT to avoid pseudo-retransmissions. However, great network latency could be introduced by applying such a mechanism in underwater communication networks. In ORIT, we propose an RTO optimization algorithm to maximize the RRG performance of the network by reducing the RTO timer. Meanwhile, we adopt the interval stop-and-wait transmission mechanism to avoid data pseudo-retransmissions caused by setting the RTO timer shorter than the RTT in narrow acoustic channels. Through adopting the RTO timer, the ORIT can achieve the best RRG performance under an RTO timer, which we call the optimal RTO timer. We compare the ORIT with other transport layer protocols in the DTN network. The results show that the ORIT with the optimal RTO timer can shorten the long propagation delay and effectively increase the data delivery rate in the underwater DTN sensor networks.

Underwater acoustic sensor networks (UASNs) have become a popular research topic, with research challenges focused on underwater communication techniques. By incorporating long end-to-end latency, high energy consumption and dynamic network topology in UASNs, many intelligent routing protocols have been proposed to solve the problem. However, shortcomings still exist, and comprehensive routing protocols are urgently needed. In this paper, we propose an adaptive Deep Q-Network-based energy- and latency-aware routing protocol (DQELR) to prolong network lifetimes in UASNs. In the DQELR, a Deep Q-Network algorithm with both off-policy and on-policy methods is adopted to make globally optimal routing decisions. Based on both the energy and depth states of nodes at different communication stages, nodes with the maximum Q-value can be selected as forwarders adaptively considering both energy and latency. A hybrid of the broadcast and unicast communication mechanisms is also designed to reduce network overhead. In addition, network topology changes can be addressed through an on-policy method that makes a new routing decision when the current route becomes corrupted. With less energy consumption and strict latency limitations, the DQELR can prolong network lifetimes in UASNs. Simulation results show that the DQELR can achieve a superior network lifetime with better latency and energy efficiency performances relative to other general schemes applied in UASNs.

Existing works design the pilot based on the mutual coherence criterion or total coherence criterion alone. However, using the mutual coherence criterion or total coherence criterion alone to design the pilot will yield the sensing matrix which corresponds to a certain pilot with a large total coherence or mutual coherence. However, the sensing matrix with a large mutual coherence or total coherence will deteriorate the sparse recovery performance and is not optimal from the perspective of the sparse recovery. Therefore, in this paper, we jointly design the pilot based on both the mutual coherence criterion and the total coherence criterion for sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. We first prove that the optimal pilot pattern with respect to the total coherence criterion is the cyclic difference set and given a pilot pattern, the pilot power allocation can be cast as a second order cone programming problem with respect to the total coherence criterion. A joint mutual coherence and total coherence pilot design algorithm is proposed to design the pilot jointly while cyclic difference set does not exist for practical OFDM systems. The proposed algorithm first optimizes the pilot pattern with the mutual coherence criterion assuming that the pilot power is equal. After obtaining the pilot pattern, the pilot power is allocated with the total coherence criterion. Simulation results have shown that the proposed pilot design algorithm can achieve the best performance in terms of normalized mean square error and bit error rate in comparison with existing pilot design methods.

Underwater glider is a cost-effective underwater unmanned vehicle with high endurance for applications in both civilian and military fields. Mobile underwater acoustic sensor networks are consisted of underwater gliders confront with several unique challenges, including high spatial-temporal uncertainty and long propagation delay of acoustic signal. In this paper, we propose an OFDMA-based MAC protocol named G-MAC for an underwater glider network, which leverages dynamic sub-channel allocation and transmission power adjustment. G-MAC is a multichannel MAC protocol dedicated for using in underwater centralized networks, which aims to maximize the network's goodput. To solve the optimal problem, G-MAC allows for concurrent data transmissions by applying Nash equilibrium to allocate transmission subchannel and related power adjustment. In addition, to reduce the negative impact from spatial uncertainty, a motion prediction method based on the characteristic of underwater glider is applied. Simulation results document that G-MAC outperforms the existing solutions in terms of network goodput and energy consumption in the representative network scenario.

Underwater acoustic sensor networks (UASNs) have attracted much attention due to their wide applications in various fields. However, as the sensor nodes are powered by batteries with limited energy and the batteries are difficult to be replaced, extending network lifetime should be taken as one of the primary objectives of network performance. In addition, due to the unreliable communication between nodes, the long propagation delay and high error rate, it is challenging to design a reliable routing protocol for UASNs. In this paper, we propose a Dempster-Shafer evidence theory-based opportunistic routing (EBOR) protocol to forward the packets to the surface sink. In EBOR, the source node considers both residual energy and packet delivery probability as evidence to determine an optimal next hop. With trust-based computation, the number of neighbors participating in forwarding is optimized to reduce energy consumption. Subsequently, the source node enables to schedule the packets transmission toward the surface sink efficiently based on the trust of nodes. Thus, the EBOR protocol can extend the network lifetime by uniformly distributing the residual energy and achieve a higher packet delivery ratio. The simulation results show that the EBOR protocol outperforms other protocols in terms of packet delivery ratio and energy efficiency.

In oceans, the limited acoustic spectrum resource is heavily shared by marine mammals and manmade systems including underwater sensor networks. In order to limit the negative impact of acoustic signal on marine mammals, we propose an environmentally friendly power control (EFPC) scheme for underwater sensor networks. EFPC allocates transmission power of sensor nodes with a consideration of the existence of marine mammals. By applying a Nash Equilibrium based utility function with a set of limitations to optimize transmission power, the proposed power control algorithm can conduct parallel transmissions to improve the network’s goodput, while avoiding interference with marine mammals. Additionally, to localize marine mammals, which is a prerequisite of EFPC, we propose a novel passive hyperboloid localization algorithm (PHLA). PHLA passively localize marine mammals with the help of the acoustic characteristic of these targets. Simulation results show that PHLA can localize most of the target with a relatively small localization error and EFPC can achieve a close goodput performance compared with an existing power control algorithm while avoiding interfering with marine mammals.

In the design of media access control (MAC) mechanism in underwater acoustic sensor networks (UASNs), due to the propagation characteristic of low-speed underwater acoustic signals, it is necessary to solve the spatial−temporal uncertainty problem. In order to avoid the multi-user access conflict in underwater networks, reduce the energy cost and improve the throughput and fairness across the network, a dynamic channel negotiation MAC mechanism based on spatial−temporal mapping of receiving queue (DCN-MAC) was proposed. DCN-MAC uses a duty cycle mechanism and implements a network management based on dynamic single node wake-up. The awakening node collects the request to send (RTS) and network status information in the network to solve the problem of space-temporal uncertainty and the highly dynamic needs of network access nodes and access services. The simulation results show that in different network scenarios, especially in those featuring high density and heavy network load, compared with the traditional underwater acoustic network MAC protocols, this protocol can effectively improve the network throughput and reduce the packet loss probability caused by multi-node conflict.

The high outage probability of an underwater wireless sensor may lead to high energy consumption. How to reduce the outage probability should be considered for underwater acoustic sensor networks (UASNs), where battery change is very difficult. Power control, which is one of the technologies to effectively reduce the outage probability, has also been developed for UASNs. However, when using the power control method with the maximum power to transmit packets, the slow fading of the signal in UASNs leads to serious accumulative interference in the receiver, which in turn leads to an even higher outage probability. Another challenge in UASNs is the complex acoustic channel condition with time-space-frequency variation and uncertain TL, which make it difficult to obtain the channel status information (CSI). To address these issues, a novel partial power control (PPC) algorithm based on outage probability minimization in UASNs is proposed. The proposed algorithm captures transmission loss (TL) using the Markov chain Monte Carlo (MCMC) method and estimates CSI in the next moment using AR prediction. The simulation results show that the proposed algorithm can effectively reduce the accumulative interference to the receiver and then reduce the outage probability by 19.3% at the maximum.

Cooperative relaying is an effective technology to improve the capacity of physical-layer security, in which the relay helps forward the received signal to the destination. In this paper, a cooperative compressive sensing and amplify-and-forward (CCS-AF) scheme, which combines the compressive sensing theory and amplify-and-forward strategy, is proposed to increase the secrecy capacity. To optimize the secrecy performance, a coalition formation algorithm based on coalitional game theory of optimal relay selection is proposed to maximize the secrecy capacity. Different to maximizing the individual utility based on the traditional pareto order, the max-coalition order rule is newly defined to guide the coalitional formation. Simulation results indicate that with the proposed algorithm, part of the relays could form a coalition to forward the information and the proposed algorithm could significantly improve the secrecy capacity of cooperative multi-relay networks.

Underwater acoustic sensor networks (UASNs) have become a hot research topic. In UASNs, nodes can be affected by ocean currents and external forces, which could result in sudden link disruption. Therefore, designing a flexible and efficient link disruption restoration mechanism to ensure the network connectivity is a challenge. In the paper, we propose a glider-assisted restoration mechanism which includes link disruption recognition and related link restoring mechanism. In the link disruption recognition mechanism, the cluster heads collect the link disruption information and then schedule gliders acting as relay nodes to restore the disrupted link. Considering the glider’s sawtooth motion, we design a relay location optimization algorithm with a consideration of both the glider’s trajectory and acoustic channel attenuation model. The utility function is established by minimizing the channel attenuation and the optimal location of glider is solved by a multiplier method. The glider-assisted restoration mechanism can greatly improve the packet delivery rate and reduce the communication energy consumption and it is more general for the restoration of different link disruption scenarios. The simulation results show that glider-assisted restoration mechanism can improve the delivery rate of data packets by 15–33% compared with cooperative opportunistic routing (OVAR), the hop-by-hop vector-based forwarding (HH-VBF) and the vector based forward (VBF) methods, and reduce communication energy consumption by 20–58% for a typical network’s setting.

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).

In this paper, compressed sensing (CS) theory is utilized in a medium-access control (MAC) scheme for wireless sensor networks (WSNs). We propose a new, cross-layer compressed sensing medium-access control (CL CS-MAC) scheme, combining the physical layer and data link layer, where the wireless transmission in physical layer is considered as a compress process of requested packets in a data link layer according to compressed sensing (CS) theory. We first introduced using compressive complex requests to identify the exact active sensor nodes, which makes the scheme more efficient. Moreover, because the reconstruction process is executed in a complex field of a physical layer, where no bit and frame synchronizations are needed, the asynchronous and random requests scheme can be implemented without synchronization payload. We set up a testbed based on software-defined radio (SDR) to implement the proposed CL CS-MAC scheme practically and to demonstrate the validation. For large-scale WSNs, the simulation results show that the proposed CL CS-MAC scheme provides higher throughput and robustness than the carrier sense multiple access (CSMA) and compressed sensing medium-access control (CS-MAC) schemes.

Physical layer security is an attractive security mechanism, which exploits the randomness characteristics of wireless transmission channel to achieve security. However, it is hampered by the limitation of the channel condition that the main channel must be better than the eavesdropper channel. To alleviate the limitation, cooperative communication is introduced. Few studies have investigated the physical layer security of the relay transmission model. In this paper, we performed some experiments to evaluate the physical layer security of a cooperative communication system, with a relay operating in decode-and-forward (DF) cooperative mode, selfish and malicious behavior in real non-ideal transmission environment. Security performance is evaluated in terms of the probability of non-zero secrecy capacity. Experiments showed some different results compared to theoretical simulation: (1) to achieve the maximum secrecy capacity, the optimal relay power according to the experiments result is larger than that of ideal theoretical results under both cooperative and selfish behavior relay; (2) the relay in malicious behavior who forwards noise to deteriorate the main channel may deteriorate the eavesdropper channel more seriously than the main channel; (3) the optimal relay positions under cooperative and selfish behavior relay cases are both located near the destination because of non-ideal transmission.