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Temperature and salinity in marine data has been studied widely to enhance the marine environmental trend analysis. This work reports a new methodology for deriving high resolution monthly averages of temperature and salinity fields for the Berkeley Canyon based on the use of a support vector regression model. The data used in this paper is from WOA13, BOA Argo and ONC observations. The experimental results show that RBF Kernel function is satisfied with huge, complex marine data. The model is of great robust and adaptability. High resolution climatological means are critical for discerning oceanic features that are of great importance not just to climate systems but also to nutrient cycling and biological habitat. The method will be satisfied with other marine area. [ABSTRACT FROM AUTHOR]

This paper introduces a new blockchain-assisted key management protocol specifically designed for clustered Underwater Acoustic Sensor Networks (UASNs). The protocol focuses on simplifying cross-cluster reauthentication for mobile underwater devices and reducing the risks of internal attacks caused by compromised nodes. To address the resource limitations of UASN devices, the protocol utilizes Elliptic Curve Qu Vanstone (ECQV) certificates, which considerably reduce Public Key Infrastructure (PKI) overhead. Moreover, it integrates the Hashed One-pass Menezes-QuVanstone (HOMQV) protocol with blockchain technology to enhance key exchange security against active attacks. The blockchain is hosted on resource-intensive surface nodes that serve as cluster heads and decentralized authorities for certificate management. Smart contracts embedded within the blockchain facilitate certificate-related operations and node trust scoring mechanism. We compared the proposed solution against a state-of-the-art benchmark and demonstrated that it imposes lower computational and communication overheads while exhibiting robustness against various attacks, as confirmed by the AVISPA tool. The solution is implemented on actual underwater/surface devices and validated experimentally. By leveraging the immutable, traceable, and fault-tolerant properties of blockchain, it establishes a secure, scalable, and efficient communication framework for mission-critical UASN applications.

Due to challenges posed by long propagation distances, high mobility, limited bandwidth, multipath propagation, and Doppler effects in underwater acoustic sensor networks (UASNs), the design of routing protocols faces numerous complexities. To enhance reliability in sparse node regions and reduce network energy consumption, this paper proposes a dependable and energy-efficient routing protocol termed WLQRP for weighting link-quality-based routing protocol. WLQRP leverages link quality information between nodes for data forwarding. When computing the link quality between nodes, it considers not only the historical transmission success ratio between nodes and their immediate relay but also incorporates the link quality between the relay and the relay's next-hop nodes. This approach effectively reduces the likelihood of encountering void regions. Moreover, the protocol judiciously selects the next-hop node to improve transmission success rates, thereby minimizing data packet retransmissions and reducing energy consumption. We conduct MATLAB simulations to evaluate the WLQRP protocol, comparing it against the VBF (vector-based forwarding) and the HH-VBF (hop-by-hop vector-based forwarding) underwater routing protocol. Experimental results demonstrate that WLQRP enhances network performance in terms of data transmission rate, energy consumption, and end-to-end delay. These findings validate the efficacy of the proposed protocol. [ABSTRACT FROM AUTHOR]

The diversity of applications supported by Underwater Sensor Networks (UWSNs) explains the success of this type of network and the increasing interest in exploiting and monitoring seas and oceans. One of the most important research fields is network deployment, since this deployment will affect all other research aspects in the UWSNs. Moreover, the initial random deployment resulting from scattering underwater sensor nodes on the network area's surface does not ensure this area's coverage and network connectivity. In this research, we propose a self-adjustment redeployment protocol that enhances network coverage and connectivity while reducing the energy consumed during network deployment. This protocol takes into account the peculiar dynamism of the underwater environment due to the water currents. First, we study the impact of these water currents on network deployment. Then, we exploit these water currents to adjust the nodes' positions to achieve total area coverage and reduce the energy consumed during the deployment by reducing the total distance traveled by the underwater sensor nodes. Simulation results show that the proposed protocol achieves a very high coverage rate (97%) and reduces the distance traveled by nodes during the deployment by 41%. [ABSTRACT FROM AUTHOR]

The use of ubiquitous and pervasive computing and technological advancements have made mobile Internet of Things, an essential component across many next-generation domains, like land vehicular networks, transportation systems, and maritime networks. These mobile IoT applications demands, which call for persistent connectivity between mobile IoT devices and IoT infrastructure, have led to the evolution of mobile fog computing. Mobile fog computing makes fog services available everywhere within the coverage of fog networks, even when users and mobile IoT devices are mobile. The massive amounts of data produced by mobile IoT devices must first be preprocessed by choosing suitable mobile fog nodes that serve as gateways before being transferred to other fog nodes and the cloud. However, the dynamic context factors, high workloads, heterogeneity, reliability, and mobility of the participating objects make gateway selection in mobile fog computing a significant issue. Therefore, in this article, we propose a novel multicriteria decision-making (MCDM) algorithm to choose the best gateway for mobile IoT devices, that takes into account factors like the distance between candidate gateways and IoT devices as well as the resources of candidate gateways like processing power, memory, and bandwidth. We simulate MFC environment using MATLAB and execute the MCDM algorithm using a case study of smart traffic management in an underwater surveillance system. The results of MCDM are compared with random gateway selection algorithm. The obtained results show that our proposed approach performs 59% better in overall packet transmission and 80% percent better network lifetimes. [ABSTRACT FROM AUTHOR]

Underwater Sensor Network (UWSN) is a network that comprises a large number of independent underwater sensor nodes to perform monitoring tasks over a given area. UWSN minimized propagation delay, bandwidth, and packet loss. However, the implementation of efficient communication is a significant problem at UWSN. Therefore, Energy and Bandwidth-aware Quadratic Ensemble Weighted Emphasis Boosting Classification (EB-QEWEBC) method for performing energy-efficient routing in UWSN is proposed. Initially, different numbers of underwater sensor nodes are considered as input. Next, the bandwidth and energy consumption of every underwater sensor node is measured. After that, classification between underwater sensor nodes is made by considering energy and bandwidth as factors using Regularized Quadratic Classifier (i.e., weak classifier) for performing routing with minimum delay. Followed by, Weighted Emphasis Boosting is utilized to ensemble weak learners to form strong learners for improving data routing performance results with the biconvex combination. Finally, after classifying the node, data packets are sent to higher energy and bandwidth-efficient underwater sensor nodes. The classification process is carried out at every underwater sensor node for transmitting data packets to the sink node with minimum delay. This method determines the energy-efficient data communication through classification and boosting to reduce the misclassification rate. Experimental results EB-QEWEBC shows a minimization of 14%, 21%, 26%, and 54% in terms of bandwidth, energy consumption, end-to-end delay, and misclassification rate as compared to state-of-art-methods respectively.

Due to challenges posed by long propagation distances, high mobility, limited bandwidth, multipath propagation, and Doppler effects in underwater acoustic sensor networks (UASNs), the design of routing protocols faces numerous complexities. To enhance reliability in sparse node regions and reduce network energy consumption, this paper proposes a dependable and energy-efficient routing protocol termed WLQRP for weighting link-quality-based routing protocol. WLQRP leverages link quality information between nodes for data forwarding. When computing the link quality between nodes, it considers not only the historical transmission success ratio between nodes and their immediate relay but also incorporates the link quality between the relay and the relay’s next-hop nodes. This approach effectively reduces the likelihood of encountering void regions. Moreover, the protocol judiciously selects the next-hop node to improve transmission success rates, thereby minimizing data packet retransmissions and reducing energy consumption. We conduct MATLAB simulations to evaluate the WLQRP protocol, comparing it against the VBF (vector-based forwarding) and the HH-VBF (hop-by-hop vector-based forwarding) underwater routing protocol. Experimental results demonstrate that WLQRP enhances network performance in terms of data transmission rate, energy consumption, and end-to-end delay. These findings validate the efficacy of the proposed protocol.

Underwater Sensor Network (UWSN) is a network that comprises a large number of independent underwater sensor nodes to perform monitoring tasks over a given area. UWSN minimized propagation delay, bandwidth, and packet loss. However, the implementation of efficient communication is a significant problem at UWSN. Therefore, Energy and Bandwidth-aware Quadratic Ensemble Weighted Emphasis Boosting Classification (EBQEWEBC) method for performing energy-efficient routing in UWSN is proposed. Initially, different numbers of underwater sensor nodes are considered as input. Next, the bandwidth and energy consumption of every underwater sensor node is measured. After that, classification between underwater sensor nodes is made by considering energy and bandwidth as factors using Regularized Quadratic Classifier (i.e., weak classifier) for performing routing with minimum delay. Followed by, Weighted Emphasis Boosting is utilized to ensemble weak learners to form strong learners for improving data routing performance results with the biconvex combination. Finally, after classifying the node, data packets are sent to higher energy and bandwidth-efficient underwater sensor nodes. The classification process is carried out at every underwater sensor node for transmitting data packets to the sink node with minimum delay. This method determines the energy-efficient data communication through classification and boosting to reduce the misclassification rate. Experimental results EB-QEWEBC shows a minimization of 14%, 21%, 26%, and 54% in terms of bandwidth, energy consumption, end-to-end delay, and misclassification rate as compared to state-of-art-methods respectively. [ABSTRACT FROM AUTHOR]

The diversity of applications supported by Underwater Sensor Networks (UWSNs) explains the success of this type of network and the increasing interest in exploiting and monitoring seas and oceans. One of the most important research fields is network deployment, since this deployment will affect all other research aspects in the UWSNs. Moreover, the initial random deployment resulting from scattering underwater sensor nodes on the network area’s surface does not ensure this area’s coverage and network connectivity. In this research, we propose a self-adjustment redeployment protocol that enhances network coverage and connectivity while reducing the energy consumed during network deployment. This protocol takes into account the peculiar dynamism of the underwater environment due to the water currents. First, we study the impact of these water currents on network deployment. Then, we exploit these water currents to adjust the nodes’ positions to achieve total area coverage and reduce the energy consumed during the deployment by reducing the total distance traveled by the underwater sensor nodes. Simulation results show that the proposed protocol achieves a very high coverage rate (97%) and reduces the distance traveled by nodes during the deployment by 41%.

Underwater Wireless Sensor Networks (UWSNs) are widely used in many fields, such as regular marine monitoring and disaster warning. However, UWSNs are still subject to various limitations and challenges: ocean interferences and noises are high, bandwidths are narrow, and propagation delays are high. Sensor batteries have limited energy and are difficult to be replaced or recharged. Accordingly, the design of routing protocols is one of the solutions to these problems. Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs, this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing (HAECR) strategy. First, this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional (3D) space. Second, sensor nodes form different competition radii based on their own relevant attributes and remaining energy. Nodes in the same layer compete freely to form clusters of different sizes. Finally, the transmission path between clusters is determined according to comprehensive factors, such as link quality, and then the optimal route is planned. The simulation experiment is conducted in the monitoring range of the 3D space. The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption, extending the network lifetime, and increasing the number of data transmissions.

Underwater sensor networks are significantly different from terrestrial networks due to certain characteristics of low bandwidth, high proliferation delay, absorption, attenuation and limited energy. These unique features cause challenges in the development of protocols for underwater sensor networks (UWSNs) by researchers. In any case, energy conservation is one of the characteristics that ought to be considered. In this paper, considering the underwater (UW) constraint, we propose an energy conservation methodology that benefits from the use of the LEACH calculation method for UWSNs. The simulation results of our proposed hierarchical clustering strategy for UW networks are compared with the hierarchical clustering strategy of LEACH of terrestrial networks and show that the proposed methodology for UWSNs matches that of LEACH for terrestrial WSNs. Similar to the LEACH protocol, the proposed methodology also reduces the total energy consumption and prolongs the life of the UWSN. In a certain round, the number of nodes that are alive and the life of the network remain the same. [ABSTRACT FROM AUTHOR]

Multimodal communication (MC) plays a crucial role in the Underwater Sensor Networks (UWSNs). The appropriate resource allocation of MC can significantly optimize the UWSNs performance. This letter investigates the optimum data transmission allocation in the underwater MC. Considering the fundamental MC tradeoff, the MC energy efficiency model is established and formulated as a mixed integer nonlinear programming problem with the provable convex optimization properties. Then this letter designs two schemes for the various data amounts to optimize the optical-acoustic transmission ratio and an optimized data collection algorithm to save the MC energy. Simulation results validate the effectiveness of this letter. [ABSTRACT FROM AUTHOR]

An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF. [ABSTRACT FROM AUTHOR]

The barrier coverage problem is generally considered for intrusion detection in underwater sensor networks (UWSNs). In conventional barrier coverage strategies in UWSNs, there is no cooperation between sensors. The information fusion is used to construct barrier coverage for higher detection probability by enhancing the sensor’s interaction capability. However, more energy is consumed for information fusion, which will shorten network lifetime. In this paper a novel high-energy-efficient barrier coverage based on nodes alliance is proposed. Based on the UWSN’s detection mode and energy consumption mode adopting nodes alliance, the relationship between the number of virtual sensors and the energy consumption is discovered. Based on this relationship, the problem of high energy efficiency is transformed into an optimization problem. By solving the optimization problem, the optimal number of physical sensors forming virtual sensors is achieved. Based on the optimal number, high detection probability and low energy consumption are taken into account simultaneously to construct barrier coverage. Both analytical and simulation studies demonstrate that the proposed strategy can provide high-energy-efficient barrier coverage. [ABSTRACT FROM AUTHOR]

An underwater sensor network (UWSN) is a wireless network that is deployed in oceans, seas, and rivers for real-time exploration of environmental conditions. The network is used to measure temperature, pressure, water pollution, oxygen level, volcanic activity, floods, and water streams. Although radio frequency (RF) is widely utilized in wireless networks, it is incompatible with the UWSN environment; therefore, other communication mechanisms have been employed to manage the underwater wireless communication among sensors, such as acoustic channels, optical waves, or magnetic induction (MI). Unlike terrestrial wireless sensor networks, UWSNs are dynamic, and sensors move according to water activity. Therefore, the network topology changes rapidly. One of the most critical challenges in UWSNs is how to collect and route the sensed data from the distributed sensors to the sink node. Unfortunately, the direct application of efficient and well-established terrestrial routing protocols is not possible in UWSNs. In this work, a balanced routing protocol based on machine learning for underwater sensor networks (BRP-ML) is proposed that considers the UWSN environmental characteristics, such as power limitations and latency, while considering the void area issue. It is based on reinforcement learning (Q-learning), which aims to reduce the network latency and energy consumption of UWSNs. The communication technique in the proposed protocol is based on the MI technique, which has many advantages, such as steady and predictable channel response and low signal propagation delay. The simulation findings validated that BRP-ML reduced latency by 18% and increased energy efficiency by 16% compared to QELAR.

Underwater sensor networks (UWSNs) are of interest to gather data from underwater sensor nodes (SNs) and deliver information to a terrestrial access point (AP) in the uplink transmission, and transfer data from the AP to the SNs in the downlink transmission. In this paper, we investigate a triple-hop UWSN in which autonomous underwater vehicle (AUV) and unmanned aerial vehicle (UAV) relays enable end-to-end communications between the SNs and the AP. It is assumed that the SN–AUV, AUV–UAV, and UAV–AP links are deployed by underwater optical communication (UWOC), free-space optic (FSO), and radio frequency (RF) technologies, respectively. Two scenarios are proposed for the FSO uplink and downlink transmissions between the AUV and the UAV, subject to water-to-air and air-to-water interface impacts; direct transmission scenario (DTS) and retro-reflection scenario (RRS). After providing the channel models and their statistics, the UWSN's outage probability and average bit error rate (BER) are computed. Besides, a tracking procedure is proposed to set up reliable and stable AUV– UAV FSO communications. Through numerical results, it is concluded that the RSS scheme outperforms the DTS one with about 200% (32%) and 80% (17%) better outage probability (average BER) in the uplink and downlink, respectively. It is also shown that the tracking procedure provides on average 480% and 170% improvements in the network's outage probability and average BER, respectively, compared to poorly aligned FSO conditions. The results are verified by applying Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) is emerging as an advance terminology for monitoring and controlling the underwater aquatic life. This technology determines the undiscovered resources present in the water through computational intelligence (CI) techniques. CI here pertains to the capability of a system to acquire a specific task from data or experimental surveillance below the water. In today's time data is considered as the identity for everything that exists in nature, whether that data is related to human beings, machines or any type of device like internet of underwater things (IoUT). The collected data should be correct, complete and fulfill the requirements of a particular task to be done. Underwater data collection is very tough because of sensors mobility due to water drift 3 meters/sec, crest and trough. A lot of packet drop also exists due to underwater conditions that hurdles the data collection process. Various techniques already exists for efficient collection of data below the water but these are not properly classified. This manuscript has summarized the concept of data collection in UWSN along with its classification based on routing. Also, a short discussion about existence of CORONA below the water along with water purification is carried out. Furthermore, some data routing approaches are also analyzed on the basis of quality of service parameters and the current challenges to be tackled during data collection are also discussed.

To solve the problem of low accuracy of a mobile node localization in an underwater sensor network, this article proposes a hybrid localization algorithm based on Doppler shift and angle of arrival (AOA) for the underwater mobile node (DAHL). This algorithm utilizes the Doppler frequency shift between the different signals received from the anchor node and the mobile node and the AOA measurement of the signal reflected by the mobile node to estimate the instantaneous position and velocity information of the mobile node through a two-stage algebraic method. In view of the existing measurement errors in the actual situation, the position and velocity estimation errors of the mobile node are optimized by introducing auxiliary parameters. Theoretical analysis and simulation experiments show that the DAHL method can effectively improve the localization accuracy of the mobile node. The localization accuracy of this algorithm can be close to the Cramér-Rao Lower Bound (CRLB) with the condition of small measurement errors.

Underwater Sensor Network (USN) is the wireless network infrastructure applicable in deep ocean to detect, collect, and transmit information to a remote data acquisition location on shore. Underwater routing protocols are used to provide route information in addition to ensuring efficient data and information collection and transmission. Some routing strategies for USNs are linked to high energy consumption, high end-to-end delay and shorter network lifetime. These issues are due to unnecessary packet flooding especially during route establishment, improper selection of next hop neighbor and frequent communication for route maintenance. Considering these challenges, this paper presents a localization free energy efficient and link reliable (E2LR) routing protocol that controls unnecessary flooding of hello packets to reduce energy consumption in the information distribution phase. Further, E2LR calculates the link quality using a composite metric for next hop selection which ultimately reduces end-to-end delay and regularly updates the energy status in order to achieve optimization during routing operations. Simulation results show that E2LR exhibit higher performance than both H2-DAB and R-ERP2R in terms of energy efficiency during information distribution phase and data forwarding phase. Moreover, E2LR also shows superior performance regarding end-to-end delay, network lifetime and packet delivery ratio than compared protocols. [ABSTRACT FROM AUTHOR]

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Magnetic induction (MI) communication is a promising technology for next-generation low-power underwater wireless sensor networks (UWSNs). Clustering algorithm design becomes an important and challenging issue in today’s MI-based UWSNs. In contrast to the conventional approaches which suffer from continuous movement of ocean current and traffic loads in different areas of the network, we consider a clustering algorithm based on the Voronoi diagram and node density distribution to improve the energy efficiency and to prolong the network lifetime. In particular, we propose a jellyfish breathing process for cluster head selection and an automatic adjustment algorithm for sensor nodes. The simulation results show that the proposed clustering algorithm achieves a high network capacity rate and a good equalization for the remaining energy.

With the rapid developments in edge devices and wireless technologies, the underwater wireless sensor networks (UWSNs) are in the process of vigorous development. In UWSNs, the traditional multi-hop data collection methods have some disadvantages such as high power consumption, severe unbalance in power consumption, and so on. In recent years, mobile edge elements (such as an autonomous underwater vehicle, AUV) are widely used in underwater data collection to solve energy consumption imbalance problems. However, the existing methods do not fully consider the efficient mobile edge computing and the real mobility model of AUV in the underwater environment. In this paper, we propose a data collection scheme based on a mobility model of mobile edge elements under water. In this model, the mobility direction and velocity are fully considered, which are close to the mobility characteristic of AUVs in the stable 3D environment. By using computing, storage, and mobility abilities of AUVs, a target selection algorithm is designed to calculate the mobility path of data collection for AUV. The theoretical analysis and experimental results show that the proposed method improves the efficiency of data collection, reduces the power consumption of nodes, and extends the network lifetime.

Underwater target search and tracking has become a technical hotspot in underwater sensor networks (UWSNs). Unfortunately, the complex and changeable marine environment creates many obstacles for localization and tracking. This paper proposes an automatic search and energy-saving continuous tracking algorithm for underwater targets based on prediction and neural network (ST-BPN). Firstly, the network contains active sensor nodes that can transmit detection signal. When analyzing the reflected signal spectrum, a modified convolutional neural network M-CNN is built to search the target. Then, based on the relationship between propagation delay and target location, a localization algorithm which can resist the influence of clock asynchrony LA-AIC is designed. Thirdly, a scheme based on consensus filtering TS-PSMCF is used to track the target. It is worth mentioning that a predictive switching mechanism, PSM, is added to the tracking process to adjust the working state of nodes. Simulation results show that the recognition accuracy of M-CNN is as high as 99.7%, the location accuracy of LA-AIC is 92.3% higher than that of traditional methods, and the tracking error of TS-PSMCF is kept between 0 m and 5 m.

An underwater sensor network (UWSN) has sparse and dynamic characteristics. In sparse and dynamic UWSNs, the traditional particle filter based on multi-rate consensus/fusion (CF/DPF) has the problems of a slow convergence rate and low filtering accuracy. To solve these problems, a tracking algorithm for sparse and dynamic UWSNs based on particle filter (TASD) is proposed. Firstly, the estimation results of a local particle filter are processed by a weighted average consensus filter (WACF). In this way, the reliability difference of state estimation between nodes in sparse and dynamic UWSN is reasonably eliminated. Secondly, a delayed update mechanism (DUM) is added to WACF, which effectively solves the problem of time synchronization between the two particle filters. Thirdly, under the condition of limited communication energy consumption, an alternating random scheme (ARS) is designed, which optimizes the mean square convergence rate of the fusion particle filter. Simulation results show that the proposed algorithm can be applied to maneuvering target tracking in sparse and dynamic UWSN effectively. Compared with the traditional method, it has higher tracking accuracy and faster convergence speed. The average estimation error of TASD is 91.3% lower than that of CF/DPF, and the weighted consensus tracking error of TASD is reduced by 85.6% compared with CF/DPF.

Underwater sensor networks are facing a great challenge in designing a routing protocol with longer network lifetime and higher packet delivery rate under the complex underwater environment. In this paper, we propose an energy-aware and void-avoidable routing protocol (EAVARP). EAVARP includes layering phase and data collection phase. During the layering phase, concentric shells are built around sink node, and sensor nodes are distributed on different shells. Sink node performs hierarchical tasks periodically to ensure the validity and real-time of the topology. It makes EAVARP apply to dynamic network environment. During the data collection phase, data packets are forwarded based on different concentric shells through opportunistic directional forwarding strategy (ODFS), even if there are voids. The ODFS takes into account the remaining energy and data transmission of nodes in the same shell, and avoids cyclic transmission, flooding, and voids. The verification and analysis of simulation results show the effectiveness of our proposed EAVARP in terms of selecting performance matrics in comparison to existing routing protocols.

Underwater wireless sensor networks (UWSNs) based on magnetic induction (MI) have been recently proposed as a promising candidate for underwater networking due to its benefits, such as small transmission delay, low vulnerability to environment changes, multipath fading negligibility, and high bandwidth. Most of the UWSN applications are location dependent and, thus, localization plays an important functionality for obtaining sensor positions. In this paper, we first study an MI-based monitoring network in shallow sea, then focus on how to design an optimal node deployment strategy and a clustering algorithm to prolong network lifetime for a 3D-UWSN by reducing the network energy consumption. Using the Voronoi diagram, we propose a high-energy node priority clustering algorithm, in which a cluster head would be selected according to the remaining energy of sensor nodes and the geometry distance among them. Moreover, in order to improve the efficiency of data collection, we use the ant colony optimization to find the shortest path for autonomous underwater vehicle. The simulation results show that the proposed approach outperforms other conventional protocols in some certain scenarios.

Summary: This paper proposes a novel estimation technique for dimensionality of deployed underwater sensor network (UWSN). In order to obtain the dimension, probing requests are sent from two anchor nodes (i.e., receivers), which are placed inside the network through buoy, to sensor nodes. Then, the sensor nodes transmit Gaussian signal as feedback to the anchor nodes. The cross‐correlation function (CCF) is determined of these Gaussian signals, which varies according to network dimension. As reference, analytical CCF of five reference networks of different dimensions is determined. Thereafter, by comparing the CCFs of unknown and reference UWSN, cost function (CF) is obtained. The lowest value of CF attained by the specific combination of the one reference and the unknown network indicates that the dimension of these networks is identical. The dimension of two unknown deployed networks is investigated to demonstrate the feasibility of the proposed technique. From the illustrated results, it is justified that the new strategy can provide correct information about the dimension of the deployed network. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor network (UWSN) has emerged as one of the most popular network technologies owing to its applicability to offshore searches, and underwater monitoring and exploration applications. It has been shown to be useful in the fields of investigations and surveillance, and in assisting with and offering solutions to water-based calamities. Reliability in the underwater environment has caused researchers to direct attention towards improving the overall efficiency and energy utilisation of the network. In the present paper, reliable node quester (RNQ) algorithm has been formulated to calculate the node reliability for numerous parameters such as success rate, transmission time, and the affordability, congestion and stability of the nodes. The present paper highlights the data-forwarding mechanism of the nodes to enhance overall network reliability by (i) reducing the packet drop rate; (ii) increasing the packet delivery ratio; and (iii) minimising the energy consumption. Simulation results further support the proposed strategy by ensuring the network lifespan and detection accuracy. [ABSTRACT FROM AUTHOR]

Node localization is one of the key technologies of Underwater Sensor Networks (UWSN). Because of the unique ocean environment conditions, beacon nodes are difficult to deploy precisely and move under the action of ocean currents and tides. Usually the beacon nodes are fixed to the sea floor through anchors and cables, and they can move within a certain range. Most existing localization algorithms do not take such mobility of beacon nodes into account, resulting in large localization errors. In order to overcome this disadvantage, this paper analyzes the characteristics of mobile-constrained beacon nodes and proposes a new localization algorithm in UWSN, namely, Mobile-constrained beacon based localization algorithm. It solves the problem that the position of beacon node is dynamic and imprecise. The unknown node can be located by the geometric relationship between the position of the anchor and the moving radius of the beacon node. In the calculation process, the polygonal area is transformed into a rectangular area, and the three-dimensional (3D) space is mapped into a two-dimensional (2D) space, the calculation process is simplified, and the feasibility of the algorithm is improved. The algorithm can be used both for 2D and 3D localization. Experiment results show that the algorithm proposed in this paper improves the localization accuracy, reduces the error rate of network node location, and has good practicality. [ABSTRACT FROM AUTHOR]

Sensor networks provide services to a broad range of applications ranging from intelligence service surveillance to weather forecasting. While most of the sensor networks are terrestrial, Underwater Sensor Networks (USN) are an emerging area. One of the unavoidable and increasing challenges for modern USN technology is tolerating faults, i.e., accepting that hardware is imperfect, and coping with it. Fault Tolerance tends to have more impact in underwater than in terrestrial environment as the latter is generally more forgiving. Moreover, reaching the malfunctioning devices for replacement and maintenance under water is harder and more costly. The current paper is the first to provide an overview of fault-tolerant, particularly cross-layer fault-tolerant, techniques in USNs. In the paper, we present a systematic survey of the techniques, introduce a taxonomy of the Fault Tolerance tasks, present a categorized list of articles, and list the open research issues within the area.

Underwater wireless sensor networks are important tools for data collection applications in underwater environments. These networks face different challenges from their terrestrial version, such as high communication delay, restricted amount of energy and high energy consumption. Topology control solutions can be beneficial to these networks since they can, among other things, save nodes energy and thus improve network lifetime. In this work, we present topology control in underwater wireless sensor networks through the use of cellular automata. We designed three solutions (TCA, TCA-F, and TCA-R) that use the concepts of cellular automata to control the topology of a network. Simulations show that the solutions are able to extend the lifetime of a network while ensuring a proper space coverage over the time. They also show how the choice of values for the parameters of each solution affects their performance. [ABSTRACT FROM AUTHOR]

Most applications of underwater cyber-physical system (UCPS) demand reliable data collection in an efficient and timely manner. However, the energy-constrained characteristic on acoustic communication makes it challenging to collect data. In this paper, we study the energy-efficient data collection for UCPS over autonomous underwater vehicle (AUV) assisted underwater acoustic sensor network. The sensor nodes perceive local physical parameters, whose lifetime is strictly restricted by local power. The AUV that has relatively sufficient energy is adopted to retrieve data from sensor nodes. To balance energy consumption and prolong underwater network lifetime, the energy optimization problem is formulated as minimizing the sum of edge lengths of a particular graph. Then, a min-weighted rigid graph based topology optimization scheme is developed for sensor deployment. With the optimized network topology, a local routing decision algorithm is developed for each sensor node to relay the collected data to the data collectors (sinks). A dynamic value-based path planning strategy is also designed for the AUV to dynamically visit data collectors to maximize the value of information (VoI) within a given time. Finally, simulation results demonstrate that the network lifetime with the topology optimization scheme can be prolonged, and the dynamic value-based path planning strategy can improve the VoI on AUV. [ABSTRACT FROM AUTHOR]

Aquaculture yield is determined by water characteristics of the farming area. The yield can be maximized by monitoring water parameters on a timely basis and reduces the management cost. Design of application based routing protocols are very important to gather domain specific sensory data for analysis and to achieve better performance in terms of routing techniques for packet delivery and energy consumption. Considering these issues, we propose a priority based routing (PBR) protocol for an underwater sensor architecture to monitor required water parameters used for aquaculture application. The underwater sensor nodes are deployed at various depths and moves with respect to water current forming restricted floating mobility model. The node mobility has significant influence on network performance and PBR protocol considers restricted floating mobility model. In the proposed protocol the sensor nodes reads the water parameters and the sensory data packets were prioritized by differentiating as emergency and regular. The high priority packets were transmitted using minimum delay path. To achieve efficient data packets forwarding to sink node with minimized energy consumption, routing parameters such as one hop delay, residual energy, buffer space, and node packet loss is considered to choose efficient neighbor nodes. The PBR protocol is simulated using DESERT underwater simulator to evaluate the network performance. The proposed PBR protocol outperforms in terms of packet delivery ratio, average energy consumption and network lifetime for aquaculture application using underwater sensor network, compared with existing routing protocols. [ABSTRACT FROM AUTHOR]

As one of the important facilities for marine exploration, as well as environment monitoring, access control, and security, underwater wireless sensor networks (UWSNs) are widely used in related military and civil fields, since the sensor node localization is the basis of UWSNs’ application in various related fields. Therefore, the research of localization algorithms based on UWSNs has gradually become one of the research hotspots today. However, unlike terrestrial wireless sensor networks (WSNs), many terrestrial monitoring and localization technologies cannot be directly applied to the underwater environment. Moreover, due to the complexity and particularity of the underwater environment, the localization of underwater sensor nodes still faces challenges, such as the localization ratio of sensor nodes, time synchronization, localization accuracy, and the mobility of nodes. In this paper, we propose a mobility-assisted localization scheme with time synchronization-free feature (MALS-TSF) for three-dimensional (3D) large-scale UWSNs. In addition, the underwater drift of the sensor node is considered in this scheme. The localization scheme can be divided into two phases. In Phase I, anchor nodes are distributed in the monitoring area, reducing the monitoring cost. Then, we address a time-synchronization-free localization scheme, to obtain the coordinates of the unknown sensor nodes. In Phase II, we use the method of two-way TOA to locate the remaining ordinary sensor nodes. The simulation results show that MALS-TSF can achieve a relatively high localization ratio without time synchronization.

Because underwater communication environments have poor characteristics, such as severe attenuation, large propagation delays and narrow bandwidths, data is normally transmitted at low rates through acoustic waves. On the other hand, as high traffic has recently been required in diverse areas, high rate transmission has become necessary. In this paper, transmission/reception timing schemes that maximize the time axis use efficiency to improve the resource efficiency for high rate transmission are proposed. The excellence of the proposed scheme is identified by examining the power distributions by node, rate bounds, power levels depending on the rates and number of nodes, and network split gains through mathematical analysis and numerical results. In addition, the simulation results show that the proposed scheme outperforms the existing packet train method.

In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node’s energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.

Underwater sensor networks have recently emerged as a promising networking technique for various underwater applications. However, the acoustic routing of underwater sensor networks in the aquatic environment presents challenges in terms of dynamic structure, high rates of energy consumption, long propagation delay, and narrow bandwidth. Therefore, it is difficult to adapt traditional routing protocols, which are known to be reliable in terrestrial wireless networks. In this study, we focus on the development of novel routing algorithms to tackle acoustic transmission problems in underwater sensor networks. The proposed scheme is based on reinforcement learning and game theory and is designed as a routing game model to provide an effective packet-forwarding mechanism. In particular, our Q-learning game paradigm captures the dynamics of the underwater sensor networks system in a decentralized, distributed manner. The results of a performance simulation analysis show that the proposed scheme can outperform existing schemes while displaying balanced system performance in terms of energy efficiency and underwater sensor networks throughput. [ABSTRACT FROM AUTHOR]

The water source, as a significant body of the earth, with a high value, serves as a hot topic to study Underwater Sensor Networks (UWSNs). Various applications can be realized based on UWSNs. Our paper mainly concentrates on the localization algorithms based on the acoustic communication for UWSNs. An in-depth survey of localization algorithms is provided for UWSNs. We first introduce the acoustic communication, network architecture, and routing technique in UWSNs. The localization algorithms are classified into five aspects, namely, computation algorithm, spatial coverage, range measurement, the state of the nodes and communication between nodes that are different from all other survey papers. Moreover, we collect a lot of pioneering papers, and a comprehensive comparison is made. In addition, some challenges and open issues are raised in our paper. [ABSTRACT FROM AUTHOR]