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Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission. [ABSTRACT FROM AUTHOR]

Underwater optical wireless communication (UOWC) is a field of research that has gained popularity with the development of unmanned underwater vehicle (UUV) technologies. Its utilization is crucial in offshore industries engaging in sustainable alternatives for food production and energy security. Although UOWC can meet the high data rate and low latency requirements of underwater video transmission for UUV operations, the links that enable such communication are affected by the inhomogeneous light attenuation and the presence of sunlight. Here, we present how the underwater spectral distribution of the light field can be modeled along the depths of eight stratified oceanic water types. We considered other established models, such as SPCTRL2, Haltrin's single parameter model for inherent optical properties, and a model for the estimation of the depth distribution of chlorophyll-a, and present insights based on transmission wavelength for the maximum signal-to-noise ratio (SNR) under different optical link parameter combinations such as beam divergence and transmit power under "daytime" and "nighttime" conditions. The results seem to challenge the common notion that the blue-green spectrum is the most suitable for underwater optical communication. We highlight a unique relationship between the transmission wavelength for the optimal SNR and the link parameters and distance, which varies with depth depending on the type of oceanic water stratification. Our analyses further highlighted potential implications for solar discriminatory approaches and strategies for routing in cooperative optical wireless networks in the photic region. [ABSTRACT FROM AUTHOR]

In marine scientific research, ocean monitoring is crucial where the batterypowered sensor devices are placed under the water to collect different information like temperature, pressure, and turbidity in underwater sensor networks (UWSNs). Thus, keeping these devices active for longer periods is challenging. In the last decades, the piezoelectric transducer (PZT) material has been used widely for constructing more environmentally friendly energy harvesting systems. The PZT harvester offers a promising solution by eliminating the need for batteries for running devices in the future with less maintenance. The PZT harvester allows the system to generate higher voltage to run low-power devices. This paper designed and developed a new renewable energy harvester system using PZT transducers for running different types of underwater sensor devices like temperature, turbidity, and obstacle sensors. The proposed PZT-based energy harvester employs a twostage amplification model for generating higher voltage and current to run multiple devices. The sensing information collected from these sensors is transmitted to the cloud which is later utilized for analysis and decisionmaking. Experiment results show the proposed PZT-based energy harvester can generate a voltage of 13 volts (V) and a current of 43.3 milliampere (mA) equivalent to 562 milliwatt (mW) which is very good to run multiple low-power underwater sensor devices. [ABSTRACT FROM AUTHOR]

Abstract Underwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss.

Underwater sensor networks hold immense potential for advancing the field of underwater target tracking, yet they encounter significant resource constraints stemming from energy storage and communication methods. In order to balance tracking accuracy and energy consumption, the authors present a distributed bearing‐only target tracking algorithm that can be used in underwater sensor networks with resource constraints. Anchored in the diffusion cubature information filter framework, this algorithm achieves fusion for non‐linear bearing measurements and state estimation. During the incremental update stage, individual nodes leverage the Posterior Cramer‐Rao Lower Bound as a metric for tracking performance. Subsequently, a strategy for selecting neighbouring nodes is introduced, ensuring tracking accuracy while efficiently kerbing energy consumption. In the diffusion update stage, a multi‐threshold event triggering mechanism is employed to partially diffuse the intermediate estimation. Additionally, an adaptive convex combination weight is proposed for cases involving partial diffusion. Through theoretical analysis, the asymptotic unbiasedness and convergence of the algorithm have been proven. Through Monte Carlo simulation experiments, the authors verify that the algorithm is superior to existing algorithms. Furthermore, the algorithm significantly reduces energy consumption in information interaction, minimising tracking accuracy loss. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks play an important role in underwater communication systems. Communication through collaborative communication is an effective way to solve critical problems in underwater communication systems. Underwater sensors are often deployed in spaces that overlap with those of marine mammals, which can adversely affect them. For this reason, in this paper, a marine mammal conflict avoidance method that can be dynamically adjusted according to the channel idle time duration and sensor node demand is designed, and the derivation of the maximum occupancy time duration is performed. Meanwhile, in addition, combining the potential of reinforcement learning in adaptive management, efficient resource optimization, and solving complex problems, this study also proposes a reinforcement learning-based relay-assisted spectrum switching method (R2S), which aims to achieve a reasonable allocation of spectrum resources in relay collaborative communication systems. The experimental results show that the method proposed in this study can effectively reduce the disturbance to marine mammals while performing well in terms of conflict probability, interruption probability, and quality of service. [ABSTRACT FROM AUTHOR]

Wireless transmission of information using water as a communication channel is one of the potential technologies for the progress of potential underwater observation systems including applications from aquaculture to the oil industry, submarine detection, instrument monitoring, and forecast of natural turbulence and study of marine life. Underwater wireless sensor networks applications are exhilarating but demanding at the same time. Some of the challenges faced by underwater networks are the movement of nodes in three-dimensional space because of which the network could not be well thought-out as a fixed topology, limited bandwidth, exceedingly low data rates, limited battery power, failures due to pollution, and corrosion, and a lot more. Additionally, as acoustic communications consume more power than terrestrial radio ones do, more sophisticated signal processing methods are needed at the receiver to make up for the channel's deficiencies. One of these difficulties is creating a routing protocol for the complexity of an underwater environment. The major aim of all these protocols is to advance data packets toward the sink to lessen the propagation delay, lessen energy expenditure, and utilize the least amount of bandwidth by restricting the broadcasting of multiple copies of the data packet. This article surveys various routing schemes and various challenges involved in the design and development of these routing schemes. [ABSTRACT FROM AUTHOR]

Preserving sensor nodes’ energy in underwater sensor networks (UWSNs) stands as a crucial priority. UWSNs find key applications in ocean monitoring, offshore oil and gas exploration, and underwater robotic operations. Our study introduces a novel approach (CTSP) utilizing clustering alongside the Traveling Salesman (TS) Protocol to optimize data routing in UWSNs while minimizing energy usage. By adjusting the pathways of data transmission among sensor nodes, this method aims to curtail the network’s overall power consumption. CTSP primarily relies on two fundamental components: TS and clustering. Leveraging the TS Protocol allows the determination of the most efficient route between any pair of sensor nodes within the network. The approach ensures that each sensor node transmits data solely to its nearest neighbor, thereby reducing the energy required for transmission. Utilizing the positions of sensor nodes as input, a clustering algorithm forms larger groups. Enhanced communication within clusters and reduced long-range communication between clusters contribute to energy conservation. Simulation results demonstrate that the proposed method significantly diminishes power consumption compared to traditional routing methods like the LEACH algorithm. Precisely, the CTSP method exhibits the potential to reduce energy usage by up to 50%, presenting a feasible option for energy-efficient data routing in underwater settings.

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission.

Underwater optical wireless communication (UOWC) is a field of research that has gained popularity with the development of unmanned underwater vehicle (UUV) technologies. Its utilization is crucial in offshore industries engaging in sustainable alternatives for food production and energy security. Although UOWC can meet the high data rate and low latency requirements of underwater video transmission for UUV operations, the links that enable such communication are affected by the inhomogeneous light attenuation and the presence of sunlight. Here, we present how the underwater spectral distribution of the light field can be modeled along the depths of eight stratified oceanic water types. We considered other established models, such as SPCTRL2, Haltrin’s single parameter model for inherent optical properties, and a model for the estimation of the depth distribution of chlorophyll-a, and present insights based on transmission wavelength for the maximum signal-to-noise ratio (SNR) under different optical link parameter combinations such as beam divergence and transmit power under “daytime” and “nighttime” conditions. The results seem to challenge the common notion that the blue-green spectrum is the most suitable for underwater optical communication. We highlight a unique relationship between the transmission wavelength for the optimal SNR and the link parameters and distance, which varies with depth depending on the type of oceanic water stratification. Our analyses further highlighted potential implications for solar discriminatory approaches and strategies for routing in cooperative optical wireless networks in the photic region.

Summary: The issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node; here Archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). [ABSTRACT FROM AUTHOR]

Underwater pollution incidents occur frequently, and obtaining accurate information about their exact location and real-time situation is helpful for promptly formulating plans to contain and mitigate the situation. Autonomously adjusting the position of sensors for optimal coverage and monitoring of regions of interest (e.g., oil spill zones, chemical contamination areas) in real time is a significant challenge. To this end, this article proposes a hunting-style underwater sensor deployment based on the level set method. This method uses a gateway-like role to calculate the boundary and other parameters of the interest region based on an energy function used for positioning sensors. Subsequently, the sensors use these parameters as the basis to complete their migration toward the boundary of the interest region. This sensor migration can gradually evolve into a hunting deployment for the interest region. This article also proposes two novel performance evaluation metrics–structural similarity and network energy balance–to evaluate the comprehensive performance of the proposed hunting-style deployment of underwater sensors. Extensive simulation experiments demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network's lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan. [ABSTRACT FROM AUTHOR]

Source localization in underwater sensor networks (UWSNs) presents complex challenges due to sensor nodes drift caused by ocean currents, non-line-of-sight (NLOS) propagation resulting from underwater multipath effects, and environmental noise. This paper proposes a practical and innovative algebraic solution based on the time difference of arrival (TDOA) for source localization in shallow seas. The proposed solution effectively addresses the issues arising from sensor position errors and multipath effects by incorporating the sea-surface reflection non-line-of-sight (SNLOS) link and optimizing the algorithm, thereby significantly improving positioning accuracy. The core concept of the method involves utilizing the weighted least squares algorithm to obtain an initial estimate of the source position, followed by direct estimation of the bias and subsequent refinement of the solution. In contrast to traditional closed-form solutions, this method avoids the introduction of intermediate parameters and directly handles the estimated bias from the previous step. Even when only considering the line-of-sight (LOS) link, the proposed solution achieves precise localization with a minimal number of sensors. Theoretical analysis demonstrates that the solution can achieve the Cramér–Rao lower bound (CRLB) accuracy under low noise conditions, and simulation results validate the superior performance of the proposed solution. [ABSTRACT FROM AUTHOR]

Underwater sensor networks (UWSNs) are an emerging field in the research area as they have potential applications starting from pollution monitoring to defense and ocean exploration. Ocean monitoring is of great importance in marine scientific research. However, battery-operated devices utilized in such systems have limited power and maintenance is difficult. So, devices used underwater suffer from many research challenges and energy issues. Apart from all the problems, harvesting energy underwater is the main limiting factor. Nonrenewable energies come from sources that may not be replenished in our lifetime. Hence, it is very much essential to use renewable energy sources. Ocean has an unlimited amount of energy like wind energy, solar power, and tidal energy. Obtaining sufficient energy is sort of difficult since devices are underwater. Researchers are continuously working on it. Water energy is quite environmentally friendly, and it is a sustainable solution for a secure energy system. This paper implements a renewable energy system using piezoelectric (PZT) sensor, which generates sufficient power for lowpower underwater devices by employing two stage amplifier circuits. Experimental outcome shows the proposed energy harvesting system can generate a maximum voltage of 10.6 V and current of 10.1 mA which is sufficient to run low power underwater device. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks possess considerable potential to monitor large and hostile underwater environments by reliably sensing, collecting, and forwarding data toward the surface sinks. Although the research community has made promising efforts, barriers such as continuous node mobility, longer delays, unavailability of location information, and energy limitations must be addressed. Taking this into account, this research aims to develop a hybrid and intelligent data collection scheme that considers node position and network characteristics during data forwarding. To accomplish the objective, the network is divided into two layers. The top layer, considered more dynamic, follows a hop-by-hop data forwarding scheme. The lower layer, experiencing stable water currents, follows a clustering-based data collection method. The proposed scheme, called Multilayer Dynamic Data Forwarding (MD2F), is suitable for large and deep underwater areas. MD2F is scalable as it uses a multi-sink architecture, while single or multiple autonomous underwater vehicles (AUVs) can be utilized depending on the area being monitored. Implementing hop-by-hop transmission and clustering-based data collection at different layers balances the network load, thereby increasing the network life. Results show that MD2F exhibits better performance when compared with Multilayer Cluster-based Energy Efficient (MLCEE) and Energy efficient and link reliable routing (E2LR) schemes, both are very close in working behavior. The results are encouraging in terms of delivery ratio, network throughput, and end-to-end delays. Alongside achieving these targets, the network also exhibits less energy consumption through load balancing.

Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network's lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim–Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network's lifetime. It also achieves a high packet delivery rate with lower energy consumption. [ABSTRACT FROM AUTHOR]

This paper introduces an improved DV-hop-based localization technique to solve the link failure problem without using extra hardware during the continuous localization process because of the uncertainty of the underwater channel, limited energy resources, and technical glitches. On the other hand, low accuracy and poor stability are the main problems of the traditional DV-hop technique. This paper also introduces a hybrid TDoA-DV hop algorithm to overcome these problems and evaluate the position of the target nodes. In this algorithm, the average hop distance is replaced with improved hop distance to suppress the hop distance error, and the Wild Horse Optimization (WHO) algorithm is used for position estimation to find out the best and fast optimum location of the target node and achieve better accuracy and stability. The experimental result of the proposed work is compared to existing works, including the traditional DV-hop, improved DV-hop, and improved shortest path +PSO. The simulation result shows that the proposed method improves the average accuracy by 67.9%, 56.24%, and 3.6%, and stability by 61.9%, 45.9%, and 5.15% approximately. The recommended technique is useful for localization in any static ad-hoc wireless or underwater sensor network due to its better accuracy and stability. [ABSTRACT FROM AUTHOR]

Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a "hot zone" problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms. [ABSTRACT FROM AUTHOR]

Considering underwater environments, this paper tackles flocking of multiple swarm robots utilizing one leader. The mission of swarm robots is to reach their goal while not colliding with a priori unknown 3D obstacles. In addition, the communication link among the robots needs to be preserved during the maneuver. Only the leader has sensors for localizing itself while accessing the global goal position. Every robot, except for the leader, can measure the relative position and the ID of its neighboring robots by utilizing proximity sensors such as Ultra-Short BaseLine acoustic positioning (USBL) sensors. Under the proposed flocking controls, multiple robots flock inside a 3D virtual sphere while preserving communication connectivity with the leader. If necessary, all robots rendezvous at the leader to increase connectivity among the robots. The leader herds all robots to reach the goal safely, while the network connectivity is maintained in cluttered underwater environments. To the best of our knowledge, our article is novel in developing underwater flocking controls utilizing one leader, so that a swarm of robots can safely flock to the goal in a priori unknown cluttered environments. MATLAB simulations were utilized to validate the proposed flocking controls in underwater environments with many obstacles. [ABSTRACT FROM AUTHOR]

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks possess considerable potential to monitor large and hostile underwater environments by reliably sensing, collecting, and forwarding data toward the surface sinks. Although the research community has made promising efforts, barriers such as continuous node mobility, longer delays, unavailability of location information, and energy limitations must be addressed. Taking this into account, this research aims to develop a hybrid and intelligent data collection scheme that considers node position and network characteristics during data forwarding. To accomplish the objective, the network is divided into two layers. The top layer, considered more dynamic, follows a hop-by-hop data forwarding scheme. The lower layer, experiencing stable water currents, follows a clustering-based data collection method. The proposed scheme, called Multilayer Dynamic Data Forwarding (MD2F), is suitable for large and deep underwater areas. MD2F is scalable as it uses a multi-sink architecture, while single or multiple autonomous underwater vehicles (AUVs) can be utilized depending on the area being monitored. Implementing hop-by-hop transmission and clustering-based data collection at different layers balances the network load, thereby increasing the network life. Results show that MD2F exhibits better performance when compared with Multilayer Cluster-based Energy Efficient (MLCEE) and Energy efficient and link reliable routing (E2LR) schemes, both are very close in working behavior. The results are encouraging in terms of delivery ratio, network throughput, and end-to-end delays. Alongside achieving these targets, the network also exhibits less energy consumption through load balancing. [ABSTRACT FROM AUTHOR]

Acoustic data communication is a powerful technology for underwater communication implementation. Underwater wireless information transmission holds immense significance for the defense, industrial and the scientific domain. In the last couple of years, a rising focus has been aimed towards acoustic wireless communication with respect to terrestrial, space and optical links since it has the potential of rendering increased data rates with reduced power and huge needs. Energy efficient data transmission has an important part to play in this practical environment. Different research approaches have been presented before to guarantee the energy efficiency data transmission in the underwater sensor network. In the earlier works, Q learning technique is found to have resulted in the time complexity and computation overhead being increased. The solution to this problem is found in the proposed research work that presents the technique known as Machine Learning based Energy Efficient High Performance Routing Protocol (MLEE-HPR). In this research work, the first step involves the transmission of acoustic data packets to underwater acoustic sensor nodes for the node information collection. This collected information will go through processing and multiobjective decision rules are derived by applying the fuzzy pareto optimality approach which will be used as input to the Modified Fuzzy SVM method for weight assignment. On the basis of the weight allocated and the network parameters obtained, the choice of the optimal route path is achieved by applying Modified Squirrel Search algorithm. The experiments on the technical research are implemented which confirms that the suggested approach works in the MATLAB simulation platform yields superior performance compared to the available research approaches. [ABSTRACT FROM AUTHOR]

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay. [ABSTRACT FROM AUTHOR]

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network’s lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

Almost all existing node selection algorithms of the underwater sensor networks (USNs) are designed by assuming ideal environments. However, the position floating of the underwater sensor nodes which caused by ocean currents cannot be ignored in practice. Aiming at solving this problem during underwater target tracking, a node selection algorithm based on multi-objective optimization under position floating was proposed in this paper. First, the error caused by position floating is converted into a floating noise. Then, as the criteria for node selection, both Fisher information matrix (FIM) and mutual information (MI) under position floating are derived by the particle filter under position floating (PF-PF). Finally, the number of nodes, the corresponding FIM and MI are set as the objective function, both nondominated sorting genetic algorithm II (NSGA-II) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used to find the optimal node selection scheme. Simulation results show that the proposed algorithm can overcome the influence of position floating, and ultimately, its tracking performance is more stable and accurate.

In the Acoustics channel, it is incredibly challenging to offer data transfer for time-sourced applications in an energy-efficient manner due to higher error rate and propagation delay. Subsequently, conventional re-transmission over any failure generally initiates significantly larger end-to-end delay, and therefore it is not probable for time-based services. Moreover, standard techniques without any re-transmission consume enormous energy. This investigation proposes a novel multi-hop energy-aware transmission-based intelligent water wave optimization strategy. It ensures reduced end-to-end while attaining potential amongst overall energy efficiency end-to-end packet delay. It merges a naturally inspired meta-heuristic approach with multi-hop routing for data packets to reach the destination. The appropriate design of this Meta heuristic-based energy-aware scheme consumes lesser energy than the conventional one-hop transmission strategy without re-transmission. However, there is no hop-by-hop re-transmission facilitated. The proposed model shows only lesser delay than conventional methods with re-transmission. This work facilitates extensive work to carry out the proposed model performance with the MATLAB simulation environment. The results illustrate that the model is exceptionally energyefficient with lesser packet delays. With 500 nodes, the packet delivery ratio of proposed model is 100%, average delay is reduced by 2%, total energy consumption is 8 J, average packet redundancy is 1.856, and idle energy is 6.9Mwh. The proposed model outperforms existing approaches like OSF, AOR, and DMR respectively. [ABSTRACT FROM AUTHOR]

Source localization in underwater sensor networks (UWSNs) presents complex challenges due to sensor nodes drift caused by ocean currents, non-line-of-sight (NLOS) propagation resulting from underwater multipath effects, and environmental noise. This paper proposes a practical and innovative algebraic solution based on the time difference of arrival (TDOA) for source localization in shallow seas. The proposed solution effectively addresses the issues arising from sensor position errors and multipath effects by incorporating the sea-surface reflection non-line-of-sight (SNLOS) link and optimizing the algorithm, thereby significantly improving positioning accuracy. The core concept of the method involves utilizing the weighted least squares algorithm to obtain an initial estimate of the source position, followed by direct estimation of the bias and subsequent refinement of the solution. In contrast to traditional closed-form solutions, this method avoids the introduction of intermediate parameters and directly handles the estimated bias from the previous step. Even when only considering the line-of-sight (LOS) link, the proposed solution achieves precise localization with a minimal number of sensors. Theoretical analysis demonstrates that the solution can achieve the Cramér–Rao lower bound (CRLB) accuracy under low noise conditions, and simulation results validate the superior performance of the proposed solution.

Low energy consumption has always been one of the core issues in the routing design of underwater sensor networks. Due to the high cost and difficulty of deployment and replacement of current underwater nodes, many underwater applications require the routing protocol design to consider the network lifetime extension problem. Based on this, we designed a new routing protocol that takes into account both low energy consumption and balanced energy consumption, and achieves effective extension of the network lifetime, called adaptive power-controlled depth-based routing protocol for underwater wireless sensor networks (APCDBRP). The protocol consists of two phases: (1) the route establishment phase and (2) the data transmission phase. In the route establishment phase, the initial path is established by the sink node broadcasting beacon packets at the maximum transmission power. The receiving nodes update their routing tables based on the beacon information and forward the beacon packets. In the data transmission phase, APCDBRP introduces a novel forwarding factor that considers both energy efficiency and energy balance. It selects the optimal next hop based on high energy efficiency and relatively abundant energy, thus extending the network’s lifetime. Additionally, APCDBRP proposes a new data protection and route reconstruction mechanism to address issues such as network topology changes due to node mobility and data transmission failures. Our simulation is based on AquaSim–Next Generation, which is a specialized tool built on the NS3 platform for researching underwater networks. Simulation results demonstrate that, compared to other typical routing protocols, APCDBRP exhibits superior performance in reducing network energy consumption and extending the network’s lifetime. It also achieves a high packet delivery rate with lower energy consumption.

Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a “hot zone” problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms.

Considering underwater environments, this paper tackles flocking of multiple swarm robots utilizing one leader. The mission of swarm robots is to reach their goal while not colliding with a priori unknown 3D obstacles. In addition, the communication link among the robots needs to be preserved during the maneuver. Only the leader has sensors for localizing itself while accessing the global goal position. Every robot, except for the leader, can measure the relative position and the ID of its neighboring robots by utilizing proximity sensors such as Ultra-Short BaseLine acoustic positioning (USBL) sensors. Under the proposed flocking controls, multiple robots flock inside a 3D virtual sphere while preserving communication connectivity with the leader. If necessary, all robots rendezvous at the leader to increase connectivity among the robots. The leader herds all robots to reach the goal safely, while the network connectivity is maintained in cluttered underwater environments. To the best of our knowledge, our article is novel in developing underwater flocking controls utilizing one leader, so that a swarm of robots can safely flock to the goal in a priori unknown cluttered environments. MATLAB simulations were utilized to validate the proposed flocking controls in underwater environments with many obstacles.

The particularity of the marine underwater environment has brought many challenges to the development of underwater sensor networks (UWSNs). This research realized the effective monitoring of targets by UWSNs and achieved higher quality of service in various applications such as communication, monitoring, and data transmission in the marine environment. After analysis of the architecture, the marine integrated communication network system (MICN system) is constructed based on the maritime wireless Mesh network (MWMN) by combining with the UWSNs. A distributed hybrid fish swarm optimization algorithm (FSOA) based on mobility of underwater environment and artificial fish swarm (AFS) theory is proposed in response to the actual needs of UWSNs. The proposed FSOA algorithm makes full use of the perceptual communication of sensor nodes and lets the sensor nodes share the information covered by each other as much as possible, enhancing the global search ability. In addition, a reliable transmission protocol NC-HARQ is put forward based on the combination of network coding (NC) and hybrid automatic repeat request (HARQ). In this work, three sets of experiments are performed in an area of 200 × 200 × 200 m. The simulation results show that the FSOA algorithm can fully cover the events, effectively avoid the blind movement of nodes, and ensure consistent distribution density of nodes and events. The NC-HARQ protocol proposed uses relay nodes for retransmission, and the probability of successful retransmission is much higher than that of the source node. At a distance of more than 2,000 m, the successful delivery rate of data packets is as high as 99.6%. Based on the MICN system, the intelligent ship constructed with the digital twins framework can provide effective ship operating state prediction information. In summary, this study is of great value for improving the overall performance of UWSNs and advancing the monitoring of marine data information. [ABSTRACT FROM AUTHOR]

High propagation delay, high error probability, floating node mobility, and low data rates are the key challenges for Underwater Wireless Multimedia Sensor Networks (UMWSNs). In this article, we propose RL-MAC, a Reinforcement Learning (RL)–based Medium Access Control (MAC) protocol for multimedia sensing in an Underwater Acoustic Network (UAN) environment. The proposed scheme uses Transmission Opportunity (TXOP) for relay nodes in a multi-hop network for improved efficiency concerning the mobility of the relays and sensor nodes. The access point (AP) and relay nodes calculate traffic demands from the initial contention of the sensor nodes. Our solution uses Q-learning to enhance the contention mechanism at the initial phase of multimedia transmission. Based on the traffic demands, RL-MAC allocates TXOP duration for the uplink multimedia reception. Further, the Structural Similarity Index Measure (SSIM) and compression techniques are used for calculating the image quality at the receiver end and reducing the image at the destination, respectively. We implement a prototype of the proposed scheme over an off-the-shelf, low-cost hardware setup. Moreover, extensive simulation over NS-3 shows a significant packet delivery ratio and throughput compared with the existing state-of-the-art. [ABSTRACT FROM AUTHOR]

Wireless underwater sensor networks have various applications—such as ocean exploration and deep-sea disaster monitoring—making them a hot topic in the research field. To cover a larger area and gather more-precise information, building large-scale underwater sensor networks has become a trend. In such networks, acoustic signals are used to transmit messages in an underwater environment. Their features of low speed and narrow bandwidth make media access control (MAC) protocols unsuitable for radio communications. Furthermore, a network consists of a large number of randomly deployed nodes, making it impossible to pre-define an optimized routing table or assign a central controller to coordinate the message propagation process. Thus, optimized routing should emerge via interaction among individual nodes in the network. To address these challenges, in this paper we propose a communication coordinator under the time division multiple access (TDMA) framework. Each node in the network is equipped with such a coordinator so that messages in the network can be sent following the shortest path in a self-organized way. The coordinator consists of a slot distributor and a forwarding guide. With the slot distributor, nodes in the sensor network occupy proper communication slots and the network finally converges to the state without communication collision. This is achieved with a set of ecological niche- and pheromone-inspired laws, which encourage nodes to occupy slots that can decrease the waiting time for a node to send a message packet while weakening the enthusiasm for a node to occupy the slots that it fails to occupy several times. With the forwarding guide, a node can send the message packet to the best successor node so that the message packet can be sent to the base station along the shortest path. It has been proven that the laws in the forwarding guide are equivalent to the Dijkstra Algorithm. Simulation experiment results indicate that with our coordinator, the network can converge to the state without collision using fewer coordination messages. In addition, the time needed to send a message to the destination is shorter than that of the classical Aloha protocol.

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neurofuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg-Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay. [ABSTRACT FROM AUTHOR]

The next-generation of wireless network 6G is envisioned to achieve seamless global full coverage, and provide ultra-high-speed and extreme low delay network to connect the Internet of Everything (IoE) on the Earth. To this end, as the space-air-ground-underwater integrated network is a solid foundation of 6G, it is critical to develop reliable and robust cross-boundary communication techniques to connect the sky and the ocean. However, due to the harsh environment-induced channel complexity of different transmission media, penetrating through the air/water interface seamlessly faces significant challenges. In this paper, we present a comprehensive review of recent progress of air/water cross-boundary communications, and group the existing works into three categories, such as 1) optical direct communication, 2) relay-based communication, and 3) non-optical direct communication, and then summarize them respectively. The three types of cross-boundary approaches have their own pros and cons which are suitable for different application scenarios. Among them, the optical direct communication has made tremendous progress recently. However, the volatile wavy surface and depth-temperature-dependent parameters make it a great challenge to build robust communication links. Moreover, the misalignment caused by the dynamics of the channel severely degrades the performance and limits their practical applications. To tackle these challenges, we summarize the complex channel characteristics, analyze the causes of the misalignment and provide potential solutions. In the end of this paper, we also discuss future research directions and hope to inspire expanding research on the air/water cross-boundary communications to further propel towards the space-air-ground-ocean integrated network in 6G. [ABSTRACT FROM AUTHOR]

Acoustic signals are used for communication in underwater sensor networks (UWSNs) because radio signals attenuate heavily when propagating in water, while optical signals have large scattering in water. Data transmission in UWSNs faces great challenges due to the characteristics of underwater acoustic channels. Moreover, high energy consumption and long latency bring about increased challenges for the designs of routing protocols in UWSNs. In this paper, we propose a routing protocol called Layer-based Energy-Efficient Routing (LEER) protocol to address the route failure problem with greedy routing, as well as the long end-to-end delay and high energy consumption problems. In LEER, each node extracts the layer field information from hello packets received and updates its own layer to avoid the problem of routing a packet to a void area. All nodes with the LEER protocol forward packets to a sink node without the need for any location information. Simulation results show that the LEER protocol outperforms the depth-based routing (DBR) protocol in terms of the delivery rate and end-to-end delay. [ABSTRACT FROM AUTHOR]

In Underwater Sensor Networks (UWSNs), the energy sources of sensor nodes are limited and difficult to recharge and solar energy cannot be used in that environment. The power issue is one of the most significant constraints in underwater sensor networks and energy balancing is essential to prolong the network lifetime. The MAC/routing protocols that are used in other types of networks may not be suitable for UWSNs due to their unique characteristics. This paper aims to overcome the energy problem by developing a new MAC/routing protocol for UWSNs called the Energy-Efficient protocol for UWSNs (EE-UWSNs). It is based on five principles to save sensor energy and to prolong the lifetime of UWSNs. These principles are using finite levels of power, applying the multi-hops transmission, narrowing the scope of transmission, applying inactivation mode, and balancing energy consumption. Using the AUVNetSim simulator, which is a Python project developed by the Massachusetts Institute of Technology (MIT), the proposed EE-UWSNs protocol was compared with well-known protocols. Simulation results proved that the proposed protocol reduces the average energy consumption of sensors by up to 68.49% compared with the other protocols. Furthermore, the average number of collisions and the end-to-end delay are enhanced. [ABSTRACT FROM AUTHOR]

This paper investigates the ordinary nodes localization problem by utilizing mobility-constrained nodes. The mobility-constrained nodes are anchored to the seabed by cables in underwater sensor networks. Many studies assume these mobility-constrained nodes’ locations as fixed, not considering their mobile properties. In the actual marine environment, they usually deviate within a restricted radius due to current fluctuations, rope tension, their gravity, etc., thus causing inaccurate localization. Moreover, underwater communication is through acoustic waves. The acoustic propagation is slow, and the propagation delay cannot be ignored. To solve these problems, particle system based localization with delay compensation is proposed, and it contains two parts. In the first step, the prior velocity information based delay compensation algorithm is designed to get the actual motion location of the target at the sampling moment. In the second step, based on the first step, the particle system based localization algorithm is proposed to narrow the localization range, where the characteristics of mobility-constrained nodes are considered. Finally, localization simulation is presented to show the effectiveness of our proposed algorithm. The proposed particle system based localization with delay compensation algorithm has higher accuracy and is more suitable for the actual underwater scenario. [ABSTRACT FROM AUTHOR]

An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been developed earlier for fish detection. However, they lack robustness to accommodate considerable differences in scenes owing to poor luminosity, fish orientation, structure of seabed, aquatic plant movement in the background and distinctive shapes and texture of fishes from different genus. With this motivation, the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN, named IDLAFD-UWSN model. The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos, particularly in blurred and crowded environments. IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network (Mask RCNN) with Capsule Network as a baseline model for fish detection. Besides, in order to train Mask RCNN, background subtraction process using Gaussian Mixture Model (GMM) model is applied. This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions. Finally,Wavelet Kernel Extreme Learning Machine (WKELM) model is utilized as a classifier model. The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98% and 97% on the applied blurred and crowded datasets respectively. [ABSTRACT FROM AUTHOR]

Underwater Sensor Network (UWSN) is gaining popularity among researchers due to its peculiar features. But there are so many challenges in the design of the UWSN system, and these are quite unsustainable due to the dynamic nature of water waves. Perhaps the most tedious challenge for UWSNs is how to transfer the data at the destination with a minimal energy rate. It can be accomplished by exploiting geographic and opportunistic routing schemes to send the data efficiently to the surface sinks in cooperation with relay nodes. With this aim, we introduce a new protocol for routing, named Geographic and Cooperative Opportunistic Routing Protocol (GCORP). In GCORP, the packets are routed from the source node to the surface sinks in coordination with intermediate relay nodes. In GCORP protocol, initially, multiple sinks-based network architecture is established. Then, a relay forwarding set is being determined by the source node on the basis of depth fitness factor. Finally, the best relay is determined through the weight calculation scheme from the relay forwarding set. We conduct the simulations in NS3 to validate the proposed GCORP routing protocol concerning different network metrics. The simulations conclude that the GCORP protocol shows better performance than existing approaches.

The software-defined networking paradigm enables wireless sensor networks as a programmable and reconfigurable network to improve network management and efficiency. However, several challenges arise when implementing the concept of software-defined networking in maritime wireless sensor networks, as the networks operate in harsh ocean environments, and the dominant underwater acoustic systems are with limited bandwidth and high latency, which render the implementation of software-defined networking central-control difficult. To cope with the problems and meet demand for high-speed data transmission, we propose a radio frequency–acoustic software-defined networking-based multi-modal wireless sensor network which leverages benefits of both radio frequency and acoustic communication systems for ocean monitoring. We first present the software-defined networking-based multi-modal network architecture, and then explore two examples of applications with this architecture: network deployment and coverage for intrusion detection with both grid-based and random deployment scenarios, and a novel underwater testbed design by incorporating radio frequency–acoustic multi-modal techniques to facilitate marine sensor network experiments. Finally, we evaluate the performance of deployment and coverage of software-defined networking-based multi-modal wireless sensor network through simulations with several scenarios to verify the effectiveness of the network. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) offer a huge number of applications, most of which require tagging the sensed data with location information. This makes localization algorithms an essential part of UWSN design. This paper presents a comprehensive survey of the recently proposed literature on localization in UWSNs. The surveyed algorithms are evaluated based on a wide-ranging set of parameters which constitute the elementary features of a localization algorithm. Moreover, in order to familiarize the readers with the basic design of the surveyed algorithms, brief description of the mode of operations of each algorithm is presented along with its strengths and weaknesses. The algorithms are divided into two categories based on their computational design i.e., centralized and distributed. Each category is further subdivided into the algorithms that consider node mobility, and those that do not. Towards the end, we present our view on the future research directions in the area of localization in UWSNs.

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay.