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Due to the increasing number of applications for various purposes, including commercial, scientific, environmental, and military ones, Underwater Wireless Sensor Networks (UWSNs) have recently attracted substantial attention from academia and enterprises in research and development. Monitoring pollutants, tactical surveillance, tsunami alerts, and offshore drilling are some important applications. Due to acoustic transmission disruptions brought on by extreme noise, extraordinarily lengthy propagation delays, a high bit error rate, a constrained bandwidth, and interference, efficient sensor communication in UWSNs is a difficult challenge. Therefore, designing efficient communication among sensors and sinks is one of the fundamental research themes in UWSNs. This paper proposes an energy-efficient optimal cluster head (CH)-based routing in UWSNs. The proposed methodology consists of three stages namely, cluster formation, CH selection, and priority-based routing. In this study, initially, the clusters are formed using a k-means clustering algorithm. Then, the CHs are selected using the Adaptive Honey Badger optimization (AHBO) algorithm, which is used to reduce energy consumption and delay. AHBO is a combination of a honey badger, Levy flight, and genetic algorithm operators. After the CH selection process, data packets are transferred toward the base station through autonomous underwater vehicles (AUVs). The efficiency of the proposed approach is analyzed based on different metrics and performance compared with the different methods. [ABSTRACT FROM AUTHOR]

Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime. [ABSTRACT FROM AUTHOR]

Abstract The underwater wireless sensor networks (UWSNs) area is a developing area of research since there are tremendous opportunities like surveying marine life, installing and monitoring optical cables, detecting earthquakes, and surveillance of territorial borders. Though many applications exist, underwater research explored to date is less than five percent as it poses many issues and challenges like water currents, temperature, pressure, water salinity, disturbance by aquatic animals, and many more factors that affect the performance of sensors deployed inside water. A significant issue UWSNs face is focusing on energy efficiency to extend the life of submerged sensors placed in isolated areas. Resolving localization concerns is a primary additional concern. In this comprehensive survey, the basics of UWSNs are covered in the introduction, followed by a thorough literature review of the existing works mainly focusing on localization, energy efficiency, Bio-inspired algorithms (BIA), and the impact of implementing Machine Learning (ML) are discussed. In concurrent sections, we have discussed attributes, parameters useful for analysis, issues and challenges in UWSN, soft computing techniques, software and hardware tools available for extended research, and opportunities in UWSN. The researchers could gain perspective pathways at the end of this survey.

Throughout history, maritime transportation has been preferred for international and intercontinental trade thanks to its lower cost than other transportation ways, which have a risk of ship accidents. To avoid these risks, underwater wireless sensor networks can be used as a robust and safe solution by monitoring maritime environment where energy resources are critical. Energy constraints must be solved to enable continuous data collection and communication for environmental monitoring and surveillance so they can last. Their energy limitations and battery replacement difficulties can be addressed with a path planning approach.This paper considers the energy-aware path planning problem with autonomous underwater vehicles by five commonly used approaches, namely, Ant Colony Optimization-based Approach, Particle Swarm Optimization-based Approach, Teaching Learning-based Optimization-based Approach, Genetic Algorithm-based Approach, Grey Wolf Optimizer-based Approach. Simulations show that the system converges faster and performs better with genetic algorithm than the others. This paper also considers the case where direct traveling paths between some node pairs should be avoided due to several reasons including underwater flows, too narrow places for travel, and other risks like changing temperature and pressure. To tackle this case, we propose a modified genetic algorithm, the Safety-Aware Genetic Algorithm-based Approach, that blocks the direct paths between those nodes. In this scenario, the Safety-Aware Genetic Algorithm-based approach provides just a 3% longer path than the Genetic Algorithm-based approach which is the best approach among all these approaches. This shows that the Safety-Aware Genetic Algorithm-based approach performs very robustly. With our proposed robust and energy-efficient path-planning algorithms, the data gathered by sensors can be collected very quickly with much less energy, which enables the monitoring system to respond faster for ship accidents. It also reduces total energy consumption of sensors by communicating them closely and so extends the network lifetime.

Underwater Wireless Sensor Networks (UWSNs) have emerged as a critical piece of technology for a wide range of maritime applications, including environmental monitoring, resource exploration, and catastrophe avoidance. An Energy Efficient Unequal Multilevel Clustering (EEUMC) algorithm tailored to UWSNs is proposed in this study. The EEUMC's primary purpose is to enhance the efficiency of data movement inside the network while decreasing the amount of energy lost. The proposed method employs a multilevel clustering framework, which divides the network into hierarchical groups based on node attributes and residual energy content. EEUMC introduces an unequal clustering technique, which differs from traditional clustering approaches. Cluster heads (CHs) are dynamically selected in this technique based on their energy levels as well as their proximity to sink nodes. The EEUMC integrates sophisticated routing protocols and adaptive data aggregation techniques in order to boost the energy economy even further. The routing algorithms route data flows across energy-efficient channels automatically, and adaptive data aggregation reduces redundant transmissions to conserve energy and keep the system functioning smoothly. This particular configuration of unequal clustering, intelligent routing, and adaptive aggregation all work together to improve data-collecting efficiency and network’s lifespan. The efficiency of the proposed EEUMC scheme was thoroughly tested through a number of simulations and head-to-head comparisons with alternative clustering approaches. When compared to more traditional approaches, the findings reveal that EEUMC greatly increases network longevity and data transmission rates. Furthermore, the scheme is robust in the sense that it can withstand changing network conditions while still ensuring a balanced consumption of energy across all nodes.

With the wide application of Underwater Wireless Sensor Networks (UWSNs) in various fields, more and more attention has been paid to deploying and adjusting network nodes. A UWSN is composed of nodes with limited mobility. Drift movement leads to the network structure's destruction, communication performance decline, and node life-shortening. Therefore, a Node Adjustment Scheme based on Motion Prediction (NAS-MP) is proposed, which integrates the layered model of the ocean current's uneven depth, the layered ocean current prediction model based on convolutional neural network (CNN)–transformer, the node trajectory prediction model, and the periodic depth adjustment model based on the Seagull Optimization Algorithm (SOA), to improve the network coverage and connectivity. Firstly, the error threshold of the current velocity and direction in the layer was introduced to divide the depth levels, and the regional current data model was constructed according to the measured data. Secondly, the CNN–transformer hybrid network was used to predict stratified ocean currents. Then, the prediction data of layered ocean currents was applied to the nodes' drift model, and the nodes' motion trajectory prediction was obtained. Finally, based on the trajectory prediction of nodes, the SOA obtained the optimal depth of nodes to optimize the coverage and connectivity of the UWSN. Experimental simulation results show that the performance of the proposed scheme is superior. [ABSTRACT FROM AUTHOR]

In Underwater Wireless Sensor Networks, ensuring precise localization of sensor nodes is crucial. Existing localization algorithms heavily rely on pre-deployed and known anchor nodes to compute and determine the positions of other nodes. However, anchor nodes may be compromised or misled in practical scenarios, resulting in localization inaccuracies. To address this challenge, a proficient iterative localization algorithm is proposed in Garg and Varna (IEEE Trans Inf Forensics Secur 7: 717–730, 2012), which selects known nodes as reference anchors to locate unknown nodes and analyzes their performance under non-collaborative attacks. Therefore, a novel localization algorithm is introduced to counter collaborative attacks, utilizing time difference of arrival ranging mapping and selective minimum gradient principles from the AdaDelta Gradient Descent (AGD) algorithm to iteratively eliminate deceptive information transmitted by interfering nodes, thereby enhancing localization accuracy. Validations demonstrate its efficacy in combating collaborative network attacks under certain conditions and significantly improving localization performance. Simulation experiments confirm its robustness even under network attacks, ensuring the reliable operation of UWSNs. [ABSTRACT FROM AUTHOR]

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation. [ABSTRACT FROM AUTHOR]

The exploration of underwater surfaces and their monitoring has become an emerging field of interest for many researchers. Localization is a crucial task in the Underwater Wireless Sensor Network (UWSN). In comparison to terrestrial sensor nodes, localizing underwater sensor nodes is more difficult. In UWSN, the communication of signals between the nodes is a challenging task due to acoustic communication and the position of the node tends to move due to a strong water current in the deep sea whereas in terrestrial the nodes are static and the localization of the node is comparatively easier. Strategic surveillance relies on accurate localization in UWSN to follow underwater assets and activities. Addressing localization issues improves situational awareness, disaster response, and mitigation in crucial areas. We reviewed Range-Based and Range-Free UWSN localization algorithms from numerous research articles and discussed trade-offs and improvement opportunities. The comparative analysis was made based on various parameters like coverage area, density of node in the network, energy consumption, localization error and computational complexity. The performance comparison helps the researchers to decide the best localization algorithm or to improve the available algorithm for UWSN.

Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different and more complex challenges than terrestrial wireless sensor networks (TWSNs). The main challenges faced by UWSNs are limited include high propagation delays, poor bandwidth, low throughput, and high energy consumption. Replacing sensor batteries in such networks becomes extremely difficult as they are usually deployed in remote areas where limited human interaction is possible. The unbalanced and inefficient usage of energy by various network nodes poses another issue, as it may reduce the applicability and feasibility of the network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) is crucial to improve the performance and lifespan of these networks. Due to the challenges mentioned earlier, this research presents an extensive analysis of several different E-ER-Ps intended for UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, as ML-based approaches have shown significant potential in resolving the intricate challenges faced by UWSNs. This paper aims to present a critical review of different E-ER-Ps from various prospects for UWSNs. To better comprehend the structure and uses of these protocols, we provide an innovative taxonomy for their classification. While ML-based protocols are evaluated for their flexibility, predictive power, and overall efficiency advancements, traditional protocols are evaluated based on their routing tactics and energy-efficiency improvements. A thorough comparative analysis highlights the advantages, disadvantages, and possible uses for different protocols. Furthermore, a critical analysis of ML’s function, incorporating intelligent and adaptive routing approaches, is presented, highlighting the technology’s potential to completely alter UWSN management. To formulate and implement E-ER-Ps for UWSNs, the article concludes by highlighting the present obstacles, including the need for real-time flexibility, resilience to environmental alters, and interaction with pre-existing network infrastructures. The development of ML-based approaches, hybrid approaches that combine conventional and ML-based methodologies, and the design of protocols that can adapt dynamically to the changing circumstances of underwater habitats are highlighted as future research objectives. This research provides the foundation for future advancements in this crucial field by presenting a comprehensive overview of the state-of-the-art UWSN E-ER-Ps.

Underwater wireless sensor networks (UWSNs) are the kind of wireless sensor networks which is deployed in an underwater environment with the help of physical sensors. UWSN has applications in different fields like the management of disasters, and navigating the underwater environment and marine species. The nodes present in the UWSN do not have an inbuilt battery, so the available energy sources must be used effectively. To retain energy efficiency in UWSN, effective clustering and routing protocols must be utilized in the UWSN environment. But, the cluster-based routing approaches are effective only for conventional wireless sensor networks. Moreover, the problem related to void hole occurs from sensor node and enhances energy dissipation which helps to minimize the life span of UWSN. So, the research proposed multi-objective sand cat swarm optimization (M-SCSO) for the selection of optimistic cluster heads (CH) and provides a better routing path for the transmission of data without a void hole problem. The parameters such as distance between neighbouring nodes, distance between base station and cluster head, residual energy, and node degree are considered while selecting optimistic CHs using MSCSO. Secondly, energyefficient routing is processed using MSCSO to deliver data packets in the shortest path. The results obtained through experimental validation represent effectiveness of proposed method. The proposed approach obtained a better packet delivery rate (PDR) of 99.32% whereas the existing emperor penguin optimized Q learning achieved PDR of 90%. [ABSTRACT FROM AUTHOR]

With the wide application of Underwater Wireless Sensor Networks (UWSNs) in various fields, more and more attention has been paid to deploying and adjusting network nodes. A UWSN is composed of nodes with limited mobility. Drift movement leads to the network structure’s destruction, communication performance decline, and node life-shortening. Therefore, a Node Adjustment Scheme based on Motion Prediction (NAS-MP) is proposed, which integrates the layered model of the ocean current’s uneven depth, the layered ocean current prediction model based on convolutional neural network (CNN)–transformer, the node trajectory prediction model, and the periodic depth adjustment model based on the Seagull Optimization Algorithm (SOA), to improve the network coverage and connectivity. Firstly, the error threshold of the current velocity and direction in the layer was introduced to divide the depth levels, and the regional current data model was constructed according to the measured data. Secondly, the CNN–transformer hybrid network was used to predict stratified ocean currents. Then, the prediction data of layered ocean currents was applied to the nodes’ drift model, and the nodes’ motion trajectory prediction was obtained. Finally, based on the trajectory prediction of nodes, the SOA obtained the optimal depth of nodes to optimize the coverage and connectivity of the UWSN. Experimental simulation results show that the performance of the proposed scheme is superior.

Due to the uniqueness of the underwater environment, traditional data aggregation schemes face many challenges. Most existing data aggregation solutions do not fully consider node trustworthiness, which may result in the inclusion of falsified data sent by malicious nodes during the aggregation process, thereby affecting the accuracy of the aggregated results. Additionally, because of the dynamically changing nature of the underwater environment, current solutions often lack sufficient flexibility to handle situations such as node movement and network topology changes, significantly impacting the stability and reliability of data transmission. To address the aforementioned issues, this paper proposes a secure data aggregation algorithm based on a trust mechanism. By dynamically adjusting the number and size of node slices based on node trust values and transmission distances, the proposed algorithm effectively reduces network communication overhead and improves the accuracy of data aggregation. Due to the variability in the number of node slices, even if attackers intercept some slices, it is difficult for them to reconstruct the complete data, thereby ensuring data security.

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation.

Summary: Underwater wireless sensor network (UWSN) is a network made up of underwater sensor nodes, anchor nodes, surface sink nodes or surface stations, and the offshore sink node. Energy consumption, limited bandwidth, propagation delay, high bit error rate, stability, scalability, and network lifetime are the key challenges related to underwater wireless sensor networks. Clustering is used to mitigate these issues. In this work, fuzzy‐based unequal clustering protocol (FBUCP) is proposed that does cluster head selection using fuzzy logic as it can deal with the uncertainties of the harsh atmosphere in the water. Cluster heads are selected using linguistic input variables like distance to the surface sink node, residual energy, and node density and linguistic output variables like cluster head advertisement radius and rank of underwater sensor nodes. Unequal clustering is used to have an unequal size of the cluster which deals with the problem of excess energy usage of the underwater sensor nodes near the surface sink node, called the hot spot problem. Data gathered by the cluster heads are transmitted to the surface sink node using neighboring cluster heads in the direction of the surface sink node. Dijkstra's shortest path algorithm is used for multi‐hop and inter‐cluster routing. The FBUCP is compared with the LEACH‐UWSN, CDBR, and FBCA protocols for underwater wireless sensor networks. A comparative analysis shows that in first node dies, the FBUCP is up to 80% better, has 64.86% more network lifetime, has 91% more number of packets transmitted to the surface sink node, and is up to 58.81% more energy efficient than LEACH‐UWSN, CDBR, and FBCA. [ABSTRACT FROM AUTHOR]

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For efficient monitoring of marine environments using underwater wireless sensor networks (UWSNs), a fundamental issue is to maximize network coverage as well as connectivity by appropriate deployment of the nodes within a given sensing area. Most of the existing node deployment schemes proposed for UWSNs, assume that sensor nodes are static and their location after deployment are fixed. However, due to ocean current and the contact of the underwater creatures, these deployed static sensor node moves from its original locations to other locations, which disrupts the network connectivity with the base stations as well as most of the targets remain uncovered. To find a solution to this issue, authors propose a coverage and connectivity aware deployment scheme for optimal placement of a set of autonomous underwater vehicles (AUVs) within UWSNs. The proposed scheme uses an improved Non-dominated Sorting Genetic Algorithm-II based metaheuristic technique with a novel fitness function which contains three parameters namely coverage quality, connected cost and network lifetime. Further, proposed scheme applies an effective encoding scheme for the population representation and devises a novel fitness function for upgrading the quantity of the AUVs as well as their location. Performance estimation of the proposed scheme and its comparison with the existing schemes with respect to convergence rate, coverage quality, connected cost, average energy consumption and network lifetime are discussed in detail. The simulation outcome confirm that the proposed approach upgrades the coverage of the network. [ABSTRACT FROM AUTHOR]

Abstract The Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge elements (such as autonomous underwater vehicles (AUVs)) provide a promising method for the data collection from sensor nodes. However, as an important part of the UIoT, underwater wireless sensor networks (UWSNs) are severely affected by the underwater dynamic environment. For instance, node locations change continuously, which significantly increases the difficulty of data collection. To solve this problem, the concept of an inevitable communication space (ICS) is proposed. The ICS is calculated by analyzing the variation in the position of nodes and the communication range. Furthermore, an ICS-based dynamic data collection algorithm (ICS-DDCA) for UIoT is proposed to collect underwater data. This method utilizes the ICS instead of the initial location of the node for data collection to further improve the performance of the algorithm and shorten the data collection time. The simulation results demonstrate that compared with the energy-efficient data collection over AUV-assisted (EEDA) and data collection algorithms based on probabilistic neighborhood (PNCS-GHA), ICS-DDCA can effectively reduce the collection time, while ensuring the full completion of data collection.

Underwater Wireless Sensor Networks (UWSNs) obtains more attention due to their wide range of applications such as underwater oil field discovery, Tsunami monitoring systems, surveillance systems, and many more. In such a resource-constrained environment, sensors are more vulnerable to malicious attacks. Node authentication and secure communication is one of the vital issues in UWSNs. In this study, a secure and lightweight key management framework for UWSNs is proposed. The proposed framework includes key generation, key distribution, revocation, and authentication mechanisms along with lightweight implementation, and scalability. We use an elliptic curve-based algorithm for key distribution, and certificate revocation list (CRL) for key revocation. We also examine the performance of the proposed framework taking into account the amount of communication overhead as well as the level of security. The simulation results show that the proposed framework provides better security with less communication overhead compared to existing frameworks. This framework can be used for secure data communication in UWSNs, which has various applications in oceanography, environmental monitoring, and military operations. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively. [ABSTRACT FROM AUTHOR]

The stratification effect underwater can lead to the localization errors in underwater acoustic sensor network. In order to compensate the stratification effect and improve the localization accuracy, we propose a localization algorithm based on the improved particle swarm optimization. Firstly, instead of approximating seawater as a homogeneous medium, this paper builds the layered medium model. Then the ray theory method is employed to compensate stratification effect. And the ranging with compensating for stratification effect is analyzed. Based on the ranging, the particle swarm optimization is improved to introduced into localization. Lastly, the optimal position of unknown nodes in underwater acoustic sensor network is obtained. Simulation results show that the proposed algorithm can effectively compensate the stratification effect and improve localization accuracy. [ABSTRACT FROM AUTHOR]

In recent years, there has been a rapid growth in Underwater Wireless Sensor Networks (UWSNs). The focus of research in this area is now on solving the problems associated with large-scale UWSN. One of the major issues in such a network is the localization of underwater nodes. Localization is required for tracking objects and detecting the target. It is also considered tagging of data where sensed contents are not found of any use without localization. This is useless for application until the position of sensed content is confirmed. This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN. The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater. Also, a detailed subdivision of these localization schemes is given. Further, these localization schemes are compared from different perspectives. The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper. At the end, the paper addresses several future directions for potential research in improving localization problems of UWSN. [ABSTRACT FROM AUTHOR]

Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an NP-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on k-coverage hole restoration and m-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate.

Advances in the different fields of application on Underwater Wireless Sensor Networks (UWSNs) makes this environment more attractive for researchers and industry. Communication in this environment faces exceptional challenges, since its data transmission is experiencing a limitation in the use of bandwidth, the presence of surrounding noise and also the long delays of acoustic propagation. It is of great benefit to design an adaptive transmission control protocol (TCP) for the underwater environment. In this work, we propose U-New Reno a transmission control protocol suitable for the marine environment wherein we consider both the adaptation of the maximum size of the congestion window and the expiry of the round-trip time (RTT). U-New Reno can achieve a very significant improvement concerning the retransmission rate of the packets and the evolution of the number of packets delivered. The performances of both metrics are closely observed during the comparison of simulation results between the standard New Reno and our proposed U-New Reno.

The balance energy consumption of nodes is a key factor to prolong network lifetime. A sensor node in Underwater Wireless Sensor Networks (UWSNs), has very limited power battery, therefore, utilizing the power battery in efficient manner is still a challenging task. To enhance network lifetime, we proposed an Energy-Efficient Regional base Cooperative Routing (EERBCR) protocol with sink mobility for UWSNs. In EERBCR, we divided the network field in 12 regions, three by four vertically and horizontally respectively. Total numbers of four mobile sinks are positioned, having equal distance to each other while 100 sensors nodes are deployed randomly. Each mobile sink travels on pre-defined straight path and covers 3 regions. All the sensor nodes are in sleeping mode, until the sink arrives to their region, upon the arrival of sink node to a region, it broadcasts a hello message. All the nodes in that region receive that message and activate themselves. When the sink is about to leave the region, it broadcasts another packet, informing the nodes about its departure so that the nodes go back into sleeping mode. The simulation results show the validity of EERBCR as it performed better than Depth-Based Routing (DBR), Energy Efficient Depth-Based Routing (EEDBR) and depth and energy-aware dominating set (DEADS) protocols. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) have a huge amount of sensors located underwater to collect data from the underwater scenario. UWSN is considered a promising method for monitoring and exploring an underwater scenario. Energy-efficient and reliable data broadcasting are considered challenging tasks, because of the limited energy source of sensors. To address this issue, an energy-efficient cluster head (CH) selection and multi-hop routing are developed in UWSN. The multi objective energy based improved jellyfish swarm optimization (MOEIJSO) is proposed to select optimal CHs from normal sensors. The input parameters of MOEIJSO are residual energy, neighbor node distance, sink distance, and CH balancing factor. Next, multi-hop routing is developed by using ant colony optimization (ACO) for delivering the data packets. Therefore, the proposed MOEIJSO-ACO method is used to improve energy efficiency while increasing data delivery. The proposed MOEIJSO-ACO method is evaluated by using the alive and dead nodes, total energy consumption, data received in base station (BS), and life expectancy. The existing researches such as metaheuristics-based clustering with a routing (MCR) for UWSN, modified low energy adaptive clustering hierarchy (MLEACH) and cooperative energy-efficient routing (CEER) approach are used to compare the MOEIJSO-ACO method. The alive nodes of the MOEIJSO-ACO are 399 for 800 rounds, which is high when compared to the MCR-UWSN. [ABSTRACT FROM AUTHOR]

Summary: Underwater Wireless Sensor Networks (UWSNs) have attracted attention from academics and industries due to many applications such as pollution monitoring, military, tsunami warning, and underwater exploration. One of the effective factors in these applications is efficient communication between underwater sensors. But this process is very challenging in UWSNs due to special conditions and underwater harsh environments. Therefore, designing routing protocols for efficient communication between sensors and sink is an important issue in UWSNs. In this context, this paper proposed a location‐free Reliable and QoS‐Aware Routing (RQAR) protocol for mobile sink UWSNs. RQAR designed using Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and does not require the position information of the sensor nodes, which is cumbersome and difficult to obtain. Proposed protocol considers multiple parameters including link quality, hop count, congestion, and residual energy for qos support, and reliable data delivery RQAR also minimizes the effects of hole occurrence by increasing the transmission range as much as possible and ensures communication stability. The simulation results using NS2 showed the superiority of RQAR in improving end‐to‐end delay, packet delivery ratio, and network throughput compared to the previous methods. [ABSTRACT FROM AUTHOR]

The Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge elements (such as autonomous underwater vehicles (AUVs)) provide a promising method for the data collection from sensor nodes. However, as an important part of the UIoT, underwater wireless sensor networks (UWSNs) are severely affected by the underwater dynamic environment. For instance, node locations change continuously, which significantly increases the difficulty of data collection. To solve this problem, the concept of an inevitable communication space (ICS) is proposed. The ICS is calculated by analyzing the variation in the position of nodes and the communication range. Furthermore, an ICS-based dynamic data collection algorithm (ICS-DDCA) for UIoT is proposed to collect underwater data. This method utilizes the ICS instead of the initial location of the node for data collection to further improve the performance of the algorithm and shorten the data collection time. The simulation results demonstrate that compared with the energy-efficient data collection over AUV-assisted (EEDA) and data collection algorithms based on probabilistic neighborhood (PNCS-GHA), ICS-DDCA can effectively reduce the collection time, while ensuring the full completion of data collection. [ABSTRACT FROM AUTHOR]

In underwater wireless sensor networks, distance-related localisation technologies rely on acquiring distance information between nodes to complete node location. Currently, ranging algorithms generally have the problems of high energy consumption and low accuracy. Therefore, a new ranging algorithm is proposed. In this algorithm, an information transmission mechanism is designed to transmit ranging information efficiently. Based on this mechanism, when the network clock is asynchronous, the information propagation time between nodes is obtained by a small number of asymmetric information sending and receiving operations. Furthermore, a selection method for the master beacon node is designed to balance the energy consumption of the nodes. To solve the problem of signal propagation path non-linearity caused by stratification effect, a signal emergence angle fast iterative method based on the known sound speed profile information is developed to calculate the distance between nodes. Simulation experiments demonstrate that the proposed algorithm is better than other state of the art algorithms in terms of energy consumption and network lifetime. In addition, the proposed algorithm can quickly solve the signal emergence angle, effectively reduce the impact of seawater sound speed layering and has good ranging accuracy in shallow seas. [ABSTRACT FROM AUTHOR]

In Underwater Wireless Sensor Networks (UWSNs), data should be transmitted to data centers reliably and efficiently. However, due to the harsh channel conditions, reliable data transmission is a challenge for large-scale UWSNs. Thus, opportunistic routing (OR) protocols with high reliability, strong robustness, low end-to-end delay, and high energy efficiency are widely applied. However, OR in UWSNs is vulnerable to routing attacks. For example, sinkhole attack nodes can attract traffic from surrounding nodes by forging information such as the distance to the sink node. In order to reduce the negative impact of malicious nodes on data transmission, we propose an intrusion detection scheme (IDS) based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm for OR (DOIDS) in this paper. DOIDS is based on small-sample IDS and is suitable for UWSNs with sparse node deployment. In DOIDS, the local monitoring mechanism is adopted. Every node in the network running DOIDS can select the trusted next hop. Firstly, according to the behavior characteristics of common routing attack nodes and unreliable underwater acoustic channel characteristics, DOIDS selected the energy consumption, forwarding, and link quality information of candidate nodes as the detection feature values. Then, the collected feature information is used to detect potential abnormal nodes through the DBSCAN clustering algorithm. Finally, a decision function is defined according to the time decay function to reduce the false detection rate of DOIDS. It makes a final judgment on whether the potential abnormal node is malicious. The simulation results show that the algorithm can effectively improve the detection accuracy rate (3% to 15% for different scenarios) and reduce the false positive rate, respectively. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Network (UWSN) is a popular technique for monitoring marine environments. It consists of several acoustic channels, sink nodes, sensor nodes and base stations. Source nodes are deployed at different depth levels of oceans for monitoring purposes. Acoustic channels communicate between a source node and a sink node. After that, the information from the sink node is delivered to the base station using an RF signal. Limited Bandwidth, Energy Consumption and propagation delay are major challenges faced by UWSN. Battery consumption of sensor nodes leads to a great impact on the performance of the underwater wireless network. Void-hole occurrence and duplication of packets from sensor nodes to sink nodes increases energy dissipation and reduces the lifespan of the UWSN. Hence energy-efficient opportunistic routing with emperor penguin optimized Q learning method (EPO-Q) for UWSN was introduced to avoid the void-hole problem and reduce energy dissipation. The proposed work was implemented in the MATLAB platform and improved performance in terms of end-to-end delay, network overhead, energy consumption, accumulated propagation distance, battery power, packet loss ratio, absorption loss, throughput, energy efficiency and packet delivery ratio. [ABSTRACT FROM AUTHOR]

The location of nodes is critical in underwater wireless sensor networks (UWSNs), which is an ocean monitoring platform. UWSNs are motivated by the popular usage of localization and play a major role in several technologies that depend primarily on innovations and localization of these nodes. Underwater node localization is a critical technology that enables the deployment of a variety of underwater applications. In this study, the underwater nodes are divided into two levels. Firstly, a clock asynchronous localization system (LS-AC) for base layer's node localization is presented. In order to eradicate the original ranging strategy's dependence on active nodes and address the problem of energy consumption, LS-AC performs in-network situation-based monitoring by relying on asynchronous clocks. Secondly, we propose a backtracking search algorithm (OTKL-BSA) based on optimal topology and knowledge learning. It is used to address the issues associated with traditional algorithms' lack of diversity and the imbalance between exploration and exploitation. Thirdly, to solve the problems that the traditional gray wolf optimizer (GWO) is prone to falling into local optimal values and has a low search efficiency, this paper proposes a GWO scheme based on hunting step size (GWO-HSS). Finally, simulation results show that the proposed algorithm outperforms SLMP, MCL-MP, MP-PSO, and MGP in aspects of localization performance. [ABSTRACT FROM AUTHOR]

Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an NP-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on k-coverage hole restoration and m-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) are essential for a number of environmental and oceanographic monitoring applications. However, they face different and more complex challenges than terrestrial wireless sensor networks (TWSNs). The main challenges faced by UWSNs are limited include high propagation delays, poor bandwidth, low throughput, and high energy consumption. Replacing sensor batteries in such networks becomes extremely difficult as they are usually deployed in remote areas where limited human interaction is possible. The unbalanced and inefficient usage of energy by various network nodes poses another issue, as it may reduce the applicability and feasibility of the network. Therefore, proposing Energy-Efficient Routing Protocols (E-ER-Ps) is crucial to improve the performance and lifespan of these networks. Due to the challenges mentioned earlier, this research presents an extensive analysis of several different E-ER-Ps intended for UWSNs. We compare contemporary approaches that use machine learning (ML) with conventional protocols, as ML-based approaches have shown significant potential in resolving the intricate challenges faced by UWSNs. This paper aims to present a critical review of different E-ER-Ps from various prospects for UWSNs. To better comprehend the structure and uses of these protocols, we provide an innovative taxonomy for their classification. While ML-based protocols are evaluated for their flexibility, predictive power, and overall efficiency advancements, traditional protocols are evaluated based on their routing tactics and energy-efficiency improvements. A thorough comparative analysis highlights the advantages, disadvantages, and possible uses for different protocols. Furthermore, a critical analysis of ML's function, incorporating intelligent and adaptive routing approaches, is presented, highlighting the technology's potential to completely alter UWSN management. To formulate and implement E-ER-Ps for UWSNs, the article concludes by highlighting the present obstacles, including the need for real-time flexibility, resilience to environmental alters, and interaction with pre-existing network infrastructures. The development of ML-based approaches, hybrid approaches that combine conventional and ML-based methodologies, and the design of protocols that can adapt dynamically to the changing circumstances of underwater habitats are highlighted as future research objectives. This research provides the foundation for future advancements in this crucial field by presenting a comprehensive overview of the state-of-the-art UWSN E-ER-Ps. [ABSTRACT FROM AUTHOR]