Harsh Underwater Environment Research Articles

Analytical modeling of UASNs for fractional power control algorithm

In Underwater Acoustic Sensor Networks (UASNs), the path loss of signal is serious due to the harsh underwater environment. Fractional power control algorithm has been widely used for the compensation of path loss. However, the randomness of underwat

Reinforcement Learning-Based Data Forwarding in Underwater Wireless Sensor Networks with Passive Mobility.

Data forwarding for underwater wireless sensor networks has drawn large attention in the past decade. Due to the harsh underwater environments for communication, a major challenge of Underwater Wireless Sensor Networks (UWSNs) is the timeliness. Furthermore, underwater sensor nodes are energy constrained, so network lifetime is another obstruction. Additionally, the passive mobility of underwater sensors causes dynamical topology change of underwater networks. It is significant to consider the timeliness and energy consumption of data forwarding in UWSNs, along with the passive mobility of sensor nodes. In this paper, we first formulate the problem of data forwarding, by jointly considering timeliness and energy consumption under a passive mobility model for underwater wireless sensor networks. We then propose a reinforcement learning-based method for the problem. We finally evaluate the performance of the proposed method through simulations. Simulation results demonstrate the validity of the proposed method. Our method outperforms the benchmark protocols in both timeliness and energy efficiency. More specifically, our method gains 83.35% more value of information and saves up to 75.21% energy compared with a classic lifetime-extended routing protocol (QELAR).

Challenges and Security Issues in Underwater Wireless Sensor Networks

With the advances in technology, there has been an increasing interest from researches and industrial institutions in the use of Underwater Wireless Sensor Networks (UWSNs). Constrained by the open acoustic channel, harsh underwater environment and the particularities of itself, UWSNs are vulnerable to a wide class of security threats and malicious attacks. A survey on threats, challenges and security issues of UWSNs are presented in this paper. In addition, current security researches and mechanisms are presented and discussed.

Cooperative Localization in Harsh Underwater Environment Based on the MC-ANFIS

In this paper, a new cooperative localization (CL) method for multiple autonomous underwater vehicles (AUVs) is proposed to address the problem of measurement outliers and communication packet loss caused by the harsh underwater environment. Combining the advantages of both the maximum correntropy criterion (MCC) and the adaptive neuro-fuzzy inference system (ANFIS), the quality of collected data can be improved by MCC and the ANFIS can be better trained. The efficacy of the proposed method in the CL of AUVs is verified by lake trial. The experimental results show that ANFIS can effectively obtain the location of AUVs based on the input data when the communication packet is lost and the combination of MCC and ANFIS provides better positioning accuracy and robustness. When the probability of measurement outliers is 2%, the proposed method reduces the averaged localization error by 80%, the standard deviation by 84%, and the maximum error difference by 73% compared with the CL method based on cubature Kalman filter(CKF). Finally, the effectiveness of this method is verified by various experiments under different measurement outliers probabilities.

An Overview of Next-Generation Underwater Target Detection and Tracking: An Integrated Underwater Architecture

Military forces of every country are trying their best to protect their motherland from the attackers. With advancement in marine technology, it has become critical to detect and track the target by obtaining active measurements before it is close enough to attack. The utilization of unmanned underwater vehicles for target tracking behavior is gaining great attention due to continuous advancement of underwater vehicular technology. Nevertheless, safe and stable communications issues among different acoustic devices are still under active investigation to reach a robust, secure, and flexible underwater networking. Moreover, due to harsh underwater environment, acoustic simulations are also time-consuming; therefore, an accurate model for target detection and tracking is a necessity. Apart from the harsh environment of underwater networks, various technologies emerging for terrestrial networking are also becoming the part of underwater networking. For instance, cognitive acoustic networks, software-defined networks, network function virtualization, cloud computing, fog computing, and internet of underwater things; all are leading to trusted next-generation underwater networks. In this paper, we first provide a comprehensive survey of unmanned underwater vehicles and different ray tracing models essential in target detection and tracking that answers several questions regarding the current necessities of underwater networks and finally, provides a solution that opens several doors for research community to excel in this area.

Localization Based on MAP and PSO for Drifting-Restricted Underwater Acoustic Sensor Networks.

Localization is a critical issue for Underwater Acoustic Sensor Networks (UASNs). Existing localization algorithms mainly focus on localizing unknown nodes (location-unaware) by measuring their distances to beacon nodes (location-aware), whereas ignoring additional challenges posed by harsh underwater environments. Especially, underwater nodes move constantly with ocean currents and measurement noises vary with distances. In this paper, we consider a special drifting-restricted UASN and propose a novel beacon-free algorithm, called MAP-PSO. It consists of two steps: MAP estimation and PSO localization. In MAP estimation, we analyze nodes’ mobility patterns, which provide the priori knowledge for localization, and characterize distance measurements under the assumption of additive and multiplicative noises, which serve as the likelihood information for localization. Then the priori and likelihood information are fused to derive the localization objective function. In PSO localization, a swarm of particles are used to search the best location solution from local and global views simultaneously. Moreover, we eliminate the localization ambiguity using a novel reference selection mechanism and improve the convergence speed using a bound constraint mechanism. In the simulations, we evaluate the performance of the proposed algorithm under different settings and determine the optimal values for tunable parameters. The results show that our algorithm outperforms the benchmark method with high localization accuracy and low energy consumption.

Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks.

With the advances in technology, there has been an increasing interest from researchers and industrial institutions in the use of underwater wireless sensor networks (UWSNs). Constrained by the open acoustic channel, harsh underwater environment, and their own particularities, UWSNs are vulnerable to a wide class of security threats and malicious attacks. However, most existing research into UWSNs has not taken security into consideration. Moreover, the existing relatively mature security mechanisms for WSNs cannot be directly utilized in UWSNs. For these reasons, this article aims to present a comprehensive overview of the particularities, constraints, attacks, challenges and current security mechanisms of UWSNs. In addition, challenging, open and hot research topics are outlined.

OMRI–MAC: Optimized Multi-transmission Receiver-Initiated MAC in Underwater Wireless Sensor Networks

Unlike terrestrial environment, the underwater environment possess additional and complicated challenges for wireless communication. For the underwater wireless communication, traditional radio wave communication is not feasible due to high channel fading and packet loss experienced high frequency communication that also reduces the communication range. Acoustic wave communication in dynamic underwater environment naturally inherits its own limitations that include low bandwidth, slow signal propagation speed, high packet loss and short communication range. Considering the UWSNs circumstances, protocols that can counter the high packet loss and low data rate are required. The MAC protocols designed for Underwater Wireless Sensor Network (UWSN) should incorporate changes in such a way that sensor nodes can have seamless and efficient communication while minimizing the effect of imposed constraints. In this paper, we therefore propose Optimized Multi-transmission Receiver-Initiated Medium Access Control (MAC) which considers harsh, lossy, and dynamic underwater environment. Our proposed method transmits optimized number of multiple packets to increase the successful packet delivery in harsh underwater environment with low data rate.

Dynamic Cluster Based Routing Protocol with Sink Mobility Support for Underwater Wireless Sensor Networks

Underwater wireless sensor networks are energy resource constrained due to the scarce battery capacity. Energy efficient routing protocol is highly demanded to be developed for such networks. It is indeed a challenging task to design routing protocol that can achieve energy efficiency due to the dynamic and harsh underwater environment. A dynamic cluster based routing protocol coupled with sink mobility support (DCMMS) is proposed. Two schemes are combined together in the protocol. One is the formation of clusters and two is the mobile sink management. The cluster formation includes cluster head election process and member association process. Each cluster member sends the sensed data to the cluster head. Multiple mobile sinks are deployed to gather data directly from cluster heads. Finally, mobile sinks send the collected data after proper aggregation to the static sinks located at the surface. Thus, sink mobility and the dynamic clustering technique together help to balance the load among the nodes thereby minimizing energy consumption to a significant extent and extending the network life span. Analytical simulations are extensively carried out to attest how the proposed protocol (DCMMS) achieves better performance with minimum energy consumption, less end to end delay and higher packet delivery ratio than its counterpart existing protocol (AA-RP).

UNISeC: Inspection, Separation, and Classification of Underwater Acoustic Noise Point Sources

Advancements in oceanic research have resulted in a plethora of activities such as undersea oil/gas exploration, environmental monitoring, sonar-based coastal surveillance, which have each increased the acoustic noise levels in the ocean and have raised concerns in the scientific community about the effect of human-generated sounds on marine life. Knowledge of the statistical characteristics of noise sources and their spatial distribution is paramount for understanding the impact on marine life as well as for regulating and policing such activities. Furthermore, studies have shown that assuming the underwater noise probability density function to be Gaussian, exponential, or Weibull is often not valid; therefore, statistically profiling the sources of the ambient noise is also essential to improve the performance of acoustic communication systems in the harsh underwater environment. In this paper, a novel solution based on the blind source separation method is proposed to enable separation of underwater acoustic noise point sources in the presence of channel propagation multipath. The proposed Underwater Noise Inspection, Separation, and Classification (UNISeC) system performs several pre- and postprocessing steps forming a novel gray-box model. Assuming there is no prior information on the noise sources, UNISeC estimates the number of such sources as well as characterizes and classifies them via a recursive pilot-aided probing method while minimizing the environmental acoustic contamination. A correlation-based characterization as well as power spectral density based classification approaches are investigated to verify the proposed method. Several scenarios are also presented and evaluated in detail via simulations.

STVF: Spatial-Temporal Variational Filtering for Localization in Underwater Acoustic Sensor Networks.

Localization is one of the critical services in Underwater Acoustic Sensor Networks (UASNs). Due to harsh underwater environments, the nodes often move with currents continuously. Consequently, the acoustic signals usually propagate with varying speeds in non-straight lines and the noise levels change frequently with the motion of the nodes. These limitations pose huge challenges for localization in UASNs. In this paper, we propose a novel localization method based on a variational filtering technique, in which the spatial correlation and temporal dependency information are utilized to improve localization performance. In the method, a state evolution model is employed to characterize the mobility pattern of the nodes and capture the uncertainty of the location transition. Then, a measurement model is used to reflect the relation between the measurements and the locations considering the dynamics of the acoustic speed and range noise. After that, a variational filtering scheme is adopted to determine the nodes’ locations, which consists of two phases: variational prediction and update. In the former phase, the coarse estimation of each node’ location is computed based on its previous location; in the latter phase, the coarse location is optimized by incorporating the measurements from the reference nodes as precisely as possible. At last, an iterative localization scheme is applied, in which a node labels itself as a reference node if the confidence of its location estimation is higher than the predefined threshold. We conducted extensive simulations under different parameter settings, and the results indicate that the proposed method has better localization accuracy compared to a typical SLMP algorithm while maintaining relatively high localization coverage. Moreover, spatial–temporal variational filtering (STVF) is more robust to the change of the parameter settings compared to SLMP.

Asynchronous Localization With Mobility Prediction for Underwater Acoustic Sensor Networks

Most applications of underwater acoustic sensor networks (UASNs) rely upon reliable location information of sensor nodes. However, the harsh underwater environment makes localization more challenging as compared to terrestrial sensor networks. This paper is concerned with a localization issue for UASNs, subject to clock asynchronization and node mobility. A hybrid architecture including autonomous underwater vehicles (AUVs), active and passive sensor nodes is designed, where AUVs act as anchor nodes to provide localization information for sensor nodes. In order to eliminate the effect of asynchronous clocks and compensate the mobility of sensor nodes, an asynchronous localization algorithm with mobility prediction is provided for active and passive sensor nodes. Then, two localization optimization problems are formulated as minimizing the sum of all measurement errors. To solve the localization optimization problems, iterative least squares estimators are designed. In addition, the convergence analysis and Crame r–Rao lower bound for the localization errors are also provided. Finally, simulation results show that the proposed localization approach can reduce the localization time by comparing with the exhaustive search-based localization method. Meanwhile, the asynchronous algorithm in this paper can effectively eliminate the impact of the clock asynchronization and node mobility.

Optimization Between Estimation Error and Transmit Energy in Cross-Correlation Based Underwater Network Cardinality Estimation

Network cardinality is a very crucial factor to ensure proper functionality of a communications network. Due to the harsh underwater environment, cardinality estimation of underwater wireless communications network using conventional protocol based methods of terrestrial networks is very challenging and inefficient. Hence, a cross-correlation based estimation approach using Gaussian signals is applied for this purpose. To obtain better performance using this estimation approach, it is assumed that the length of the signals is infinity, which is practically impossible because it implies infinite transmit energy. For this reason, it is required to investigate the effect of various signal length (N s) on estimation performance. In process of achieving this goal, a relation between the N s and estimation error is derived in this paper. Using this relationship, optimization between estimation error and transmit energy is obtained as N s is an energy related term in the estimation process.

Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs.

Underwater wireless sensor networks (UWSNs) facilitate a wide range of aquatic applications in various domains. However, the harsh underwater environment poses challenges like low bandwidth, long propagation delay, high bit error rate, high deployment cost, irregular topological structure, etc. Node mobility and the uneven distribution of sensor nodes create void holes in UWSNs. Void hole creation has become a critical issue in UWSNs, as it severely affects the network performance. Avoiding void hole creation benefits better coverage over an area, less energy consumption in the network and high throughput. For this purpose, minimization of void hole probability particularly in local sparse regions is focused on in this paper. The two-hop adaptive hop by hop vector-based forwarding (2hop-AHH-VBF) protocol aims to avoid the void hole with the help of two-hop neighbor node information. The other protocol, quality forwarding adaptive hop by hop vector-based forwarding (QF-AHH-VBF), selects an optimal forwarder based on the composite priority function. QF-AHH-VBF improves network good-put because of optimal forwarder selection. QF-AHH-VBF aims to reduce void hole probability by optimally selecting next hop forwarders. To attain better network performance, mathematical problem formulation based on linear programming is performed. Simulation results show that by opting these mechanisms, significant reduction in end-to-end delay and better throughput are achieved in the network.

An Efficient Source–Channel Coding for Wireless Image Transmission Over Underwater Acoustic Channel

In this paper, a complete system for image transmission in harsh underwater environment is proposed. The key to increase the performance of the system is the use of an efficient image compression algorithm with a bandwidth-efficient modulation technique. The wavelet packet (WP) decomposition is used to get the best image representation and the set partitioning in hierarchical trees is applied on the WP coefficients. The parental conflicts are resolved, the parent–child relationships are adapted and thus the similarities between cross-subbands are preserved. Reed–Solomon is used for forward error correction to combat with the errors in wireless transmission. Orthogonal frequency division multiplexing with differential quadrature phase shift keying is used to transmit the generated bit stream. Effective image quality metrics are used for objective evaluation. Results show that the proposed system manages to transmit images over the limited bandwidth, and to effectively minimize the perceptual degradation.

Relay Selection in Underwater Acoustic Cooperative Networks: A Contextual Bandit Approach

Cooperative relay transmission is an attractive architecture for underwater acoustic networks. However, designing relay selection policies in the harsh underwater environment is difficult. In this letter, we model relay selection as a contextual bandit problem—an important extension of multi-armed bandit. Through this way, we can achieve relay selection based on a bit of contextual communication environment information about relay nodes instead of instantaneous or statistical channel state information. Our proposed relay selection technique enables highly stable performance of the cooperative system in a complex and changeable underwater environment, and the process of relay selection can be simplified and easily facilitate efficient cooperative transmission. Simulation results illustrate the effectiveness and the robustness of this relay selection technique.

A Distributed and Energy-Efficient Algorithm for Event K-Coverage in Underwater Sensor Networks.

For event dynamic K-coverage algorithms, each management node selects its assistant node by using a greedy algorithm without considering the residual energy and situations in which a node is selected by several events. This approach affects network energy consumption and balance. Therefore, this study proposes a distributed and energy-efficient event K-coverage algorithm (DEEKA). After the network achieves 1-coverage, the nodes that detect the same event compete for the event management node with the number of candidate nodes and the average residual energy, as well as the distance to the event. Second, each management node estimates the probability of its neighbor nodes’ being selected by the event it manages with the distance level, the residual energy level, and the number of dynamic coverage event of these nodes. Third, each management node establishes an optimization model that uses expectation energy consumption and the residual energy variance of its neighbor nodes and detects the performance of the events it manages as targets. Finally, each management node uses a constrained non-dominated sorting genetic algorithm (NSGA-II) to obtain the Pareto set of the model and the best strategy via technique for order preference by similarity to an ideal solution (TOPSIS). The algorithm first considers the effect of harsh underwater environments on information collection and transmission. It also considers the residual energy of a node and a situation in which the node is selected by several other events. Simulation results show that, unlike the on-demand variable sensing K-coverage algorithm, DEEKA balances and reduces network energy consumption, thereby prolonging the network’s best service quality and lifetime.

LRP: Link quality‐aware queue‐based spectral clustering routing protocol for underwater acoustic sensor networks

SummaryRecently, underwater acoustic sensor networks (UASNs) have been considered as a promising approach for monitoring and exploring the oceans in lieu of traditional underwater wireline instruments. As a result, a broad range of applications exists ranging from oil industry to aquaculture and includes oceanographic data collection, disaster prevention, offshore exploration, assisted navigation, tactical surveillance, and pollution monitoring. However, the unique characteristics of underwater acoustic communication channels, such as high bit error rate, limited bandwidth, and variable delay, lead to a large number of packet drops, low throughput, and significant waste of energy because of packets retransmission in these applications. Hence, designing an efficient and reliable data communication protocol between sensor nodes and the sink is crucial for successful data transmission in underwater applications. Accordingly, this paper is intended to introduce a novel nature‐inspired evolutionary link quality‐aware queue‐based spectral clustering routing protocol for UASN‐based underwater applications. Because of its distributed nature, link quality‐aware queue‐based spectral clustering routing protocol successfully distributes network data traffic load evenly in harsh underwater environments and avoids hotspot problems that occur near the sink. In addition, because of its double check mechanism for signal to noise ratio and Euclidean distance, it adopts opportunistically and provides reliable dynamic cluster‐based routing architecture in the entire network. To sum up, the proposed approach successfully finds the best forwarding relay node for data transmission and avoids path loops and packet losses in both sparse and densely deployed UASNs. Our experimental results obtained in a set of extensive simulation studies verify that the proposed protocol performs better than the existing routing protocols in terms of data delivery ratio, overall network throughput, end‐to‐end delay, and energy efficiency.

A Cross-Layer MAC Protocol for Underwater Acoustic Sensor Networks

One of the major challenges for the deployment of underwater acoustic sensor networks is the development of a medium access control (MAC) protocol catering for the harsh underwater environment. In particular, an underwater MAC protocol should provide high end-to-end throughput, low channel access delay, and fair share of the scarce network bandwidth. In this paper, a cross-layer MAC protocol is proposed. It interacts with a price-based rate allocation scheme at the network layer. To accurately reflect the clique constraint of the wireless medium, the clique-based price is generalized to act as the congestion signal, which controls the end-to-end rates of multihop flows. The MAC protocol then schedules contention-free packet transmissions of single-hop subflows in each maximum clique. Both the MAC protocol and rate allocation algorithm are simple and direct, thus owning low computational complexity. Through analysis and simulation, we show that the proposed MAC protocol enables multihop flows to acquire the max-min fair share of the network bandwidth from the end-to-end perspective.

Weight Self-Adjustment Adams Implicit Filtering Algorithm for Attitude Estimation Applied to Underwater Gliders

High-accuracy attitude estimation plays an important role in gliding with long endurance for an underwater glider. Because microelectromechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption, they are used as main sensors to determine navigation information. However, in the complicated and harsh underwater environment, the performances of MEMS sensors degrade and errors will become larger. Moreover, acceleration or deceleration while gliders going up and down, sudden vibration of gliders due to inevitable disturbances will bring larger errors for sensors. So it is difficult to acquire the high accuracy attitude calculated by inertial measurement unit. In order to solve the above problem, first, a novel weight self-adjustment extended Kalman filtering method, which can adjust the weight autonomously through estimating adaptively measurement noise, is proposed to perform the optimal error estimation. Moreover, a fusion method that integrates the Adams implicit formula with the weight self-adjustment filtering method is proposed to achieve the more improvement in attitude estimation accuracy. The performance of this proposed algorithm is evaluated by the theoretical proofs and simulations. Subsequently, it is tested by the ship experiments and the lake trials. The results show that this proposed algorithm has a better performance in terms of attitude estimation accuracy than extended Kalman filtering (EKF)-only and self-adjustment EKF in this paper. Meanwhile, this algorithm has good robustness for attitude calculation even though pitch angle changes large.