With the development of the Internet of underwater things (IoUT), two critical problems have been prominent, i.e. the energy constraint of underwater devices and large demand for data collection. In this paper, we introduce an autonomous underwater vehicle (AUV) aided underwater acoustic sensor networks (UWSNs) to solve these problems. To improve the performance of UWSNs, we formulate an optimization problem to maximize the energy consumption utility, which is defined to balance the energy consumption and network throughput. To solve this optimization problem, we decompose it into four parts. Firstly, due to the constraint of communication distance, we construct a cluster-based network and formulate the selection of cluster heads as a maximal clique problem (MCP). Secondly, the clustering algorithm is proposed. Thirdly, we design a novel media access control (MAC) protocol to coordinate data transmission between AUV and cluster heads, among intra-cluster nodes, as well as among inter-cluster nodes. Finally, path planning of AUV is formulated as a traveling salesman problem to minimize AUV travel time. Based on the above analysis, two algorithms, namely AUV-aided energy-efficient data collection (AEEDCO) and approximate AUV-aided energy-efficient data collection (AEEDCO-A), are developed accordingly. Simulation results show that the proposed algorithms perform well and are very promising in UWSNs with demand for large-scale communication, large system capacity, long term monitoring, and high data traffic load.

This paper mainly investigates the application of underwater acoustic sensor networks (UASNs), which are characterized by large-scale, sparse distribution, and varying traffic loads. Since the underwater acoustic channel is known for limited bandwidth, time variation, and long propagation delays, the strategy to access the common communication medium is required to improve the performance of UASNs. This paper proposes a delay and queue aware adaptive scheduling-based medium access control (DQA-MAC) protocol for UASNs. It combines adaptive scheduling transmission, reduction of handshaking packets, and concurrent transmission with the propose of improving the performance of network throughput, shortening end-to-end delay, reducing average energy consumption, and enhancing the fairness of transmission. Data transmission time is scheduled based on the information of propagation delays and the number of data packets waiting in each node queue. Furthermore, the strategy of concurrent transmission is implemented to leverage the long propagation delays. At last, reducing the number of handshaking packets is achieved with the approach of exchanging information by specially designed packets frames. The simulation results show that the proposed protocol outperforms the related traditional protocols in networks with varying traffic loads.

Communication across the water–air interface is an open research problem. A common solution is to place a relay on the water–air interface, which transmits received underwater acoustic signals to shore via electromagnetic waves. However, this solution has limitations, such as surface relays easily floating away or underwater relays becoming resource-intensive due to frequent surfacing, which make water–air communications unstable and costly. In this article, we investigate a recently proposed cross-medium communication technique, translational acoustic radio frequency (TARF) communications, in which underwater nodes can directly communicate with airborne nodes via acoustic waves and millimeter waves. For the first time, we introduce a closed-form, end-to-end channel and present its experimental validation. The high-frequency TARF communication is achievable with a frequency of up to 4.4 kHz. In the experiment deployment, underwater acoustic signals traveled as pressure waves, which produced water surface waves when they impinged on the water–air boundary. Airborne radar was employed to measure and decode these water surface waves. Experimental results and theoretical analysis demonstrated that 1) the cross-medium communication channel is frequency selective and time invariant; 2) cross-medium communications with both single-carrier and multicarrier signals are feasible; and 3) high-frequency signals from 3.6 to 4.4 kHz can be detected, which was considered difficult previously. This study provides a theoretical and experimental basis for achieving cross-medium communications between deep-sea moving objects, such as autonomous underwater vehicles and airborne nodes.

Underwater acoustic sensor networks (UASNs) have emerged as a viable networking approach due to their numerous aquatic applications in recent years. As a vital component of UASNs, routing protocols are essential for ensuring reliable data transmissions and extending the longevity of UASNs. Recently, several clustering-based routing protocols have been proposed to reduce energy consumption and overcome the resource constraints of deployed sensor nodes. However, they rarely consider the hot-spots problem and the sink node isolation problem in the multihop underwater sensor networks. In this paper, we propose a Q-learning-based hierarchical routing protocol with unequal clustering (QHUC) for determining an effective data forwarding path to extend the lifespan of UASNs. First, a hierarchical network structure is constructed for initialization. Then a combination of unequal clustering and the Q-learning algorithm is applied to the hierarchical structure to disperse the remaining energy more evenly throughout the network. With the use of the Q-learning algorithm, a global optimal CH and next-hop can be determined better than a greedy one. In addition, the Q-value that guarantees the optimal routing decisions can be computed without incurring any additional costs by combining the Q-learning algorithm with clustering. The simulation results show that the QHUC can achieve an efficient routing and prolong the network lifetime significantly.

With the rapid development of autonomous underwater vehicles (AUVs) and the continuous improvement of marine exploration requirements, AUV formation has emerged as a promising technique for performing underwater tasks with greater flexibility, adaptability, and scalability. To achieve AUV formation control, underwater acoustic communication networks (UACNs) are widely used to support information exchange among AUVs. Considering broadcast communications in fully connected mobile UACNs, existing medium access control (MAC) protocols face severe packet collision risk or low reuse efficiency. To improve the timeliness and reliability of the information exchange among AUVs, we develop an efficient scheduling-based adaptive broadcasting MAC (AB-MAC) protocol. The AB-MAC protocol is incompletely centralized, which can be reflected in two aspects. First, to ensure the consistency of the transmission schedule in the network, the AB-MAC protocol selects a control node for each frame to schedule the transmission of all nodes according to the updated network topology. The slot lengths and transmission sequences are changeable in each frame to avoid packet collisions and minimize frame lengths. Second, to further improve the efficiency and robustness of the protocol, all nodes adaptively adjust their transmission time by integrating the information contained in their previously received control packets and ordinary packets. The transmission scheduling problem is formulated as a combinatorial optimization problem, which can be solved by our proposed improved Genetic Algorithm. Numerical results demonstrate that the AB-MAC protocol not only performs well in terms of network throughput and average update interval but also has a high level of robustness even in low-quality underwater acoustic channels.

With the development of the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have become an enabling system to support real-time and continuous data transmission. Existing scheduling-based medium access control (MAC) protocols ignored the limitations between the generation time and the transmission time of forwarding packets. This results in unavailability of some scheduled slots, and makes packets endure more waiting time on relay nodes and thereafter longer end-to-end delay. To reduce transmission latency, this article develops a novel scheduling-based MAC protocol, which schedules slots in the packet level. All packets to be generated and transmitted in a frame are predicted and scheduled. Considering our defined packet collision constraint and traffic-flow constraint, we formulate the slot scheduling problem in a frame into a combinatorial optimization problem, which ensures that all source packets transmitted in a frame can be delivered to the sink node with the minimum average end-to-end delay within the same frame. To solve this problem, two algorithms are proposed, namely, an optimal packet-level slot scheduling (PLSS) algorithm and a heuristic approximate PLSS (PLSS-A) algorithm. The performances of our proposed protocol using both algorithms are evaluated with different network scales, packet lengths, and offered traffic loads. Numerical results demonstrate that both PLSS and PLSS-A perform well in terms of average end-to-end delay and service fairness, and have an advantage in the network throughput in large-scale networks. Our proposed PLSS MAC protocol is predicted to be promising in large-scale UASNs with demand for real-time and long-term monitoring.