Underwater Mines Detection by Sonar Signals: History
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Saad A. Al-Ahmadi

Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. 

  • underwater mine detection
  • acoustic wireless sensor network
  • sonar signal

1. Introduction

The recognition and detection of underwater mines is an active research field motivated by the need to clear mines, due to their harmful effects on the environment [1]. An underwater mine is a destructive object that represents a significant threat to human and marine life [2][3]. Many systems for detecting underwater mines have been developed to reduce the negative impact of their explosion. However, almost all of the existing methods require sophisticated, expensive equipment to explore the sea and/or human operators to maintain an ideal system. Therefore, a detection system is needed that improves the efficiency of the mine clearance process, with a significant reduction in the operational time, cost, and the system operator’s risk of injury or loss of life, and with high detection accuracy.
Wireless sensor networks (WSN) hold great potential for aquatic environment monitoring, since they can sense, gather, and transmit data without a physical connection [2][4]. Although, in a roundabout way, this has led to the development of a new self-driven device called underwater wireless sensor networks (UWSNs) [5], they are considered an alternative to manual operations, such as cable interactions and aquatic systems, for implementations (e.g., self-directed underwater vehicles (AUVs) and autonomous underwater vehicle management) [1]. These systems provide an attractive solution for the low-cost continuous monitoring of underwater environments [4][6][7][8]. Underwater acoustic sensor networks (UWASN) can be applied for the detection of underwater mines. Furthermore, these devices incorporate sensors and other components that can send and receive different signals. They can communicate through acoustic waves, which are used to build and deploy UWSN systems in deep underwater settings.
The sensor nodes have strong limitations in their processing ability, embedded battery power, wireless bandwidth, and storage space. The major obstacle that calls into question the feasibility of applications built on these sensors is the energy constraint. Therefore, in order to extend the sensor battery lifetime, a low-complexity scheme for data processing and communication is required [9][10]. The clustering approach is one of the practical solutions to managing network energy consumption efficiently [11]. It also helps to distribute the energy consumption among the nodes in the network. The working mechanism for this approach involves grouping the sensor nodes into the cluster and electing one of these nodes to be the cluster head. The cluster head is responsible for gathering the data from its members and sending them to the base station.
In most cases, the nodes will be deployed densely to cover all of the required areas, which makes some of the nodes enter sleep mode, thereby reducing the energy consumption. The use of a cluster-based architecture helps to share the processing load via the sensors of the cluster, which consequently reduces the per-node energy consumption and contributes to extending the network lifetime. Furthermore, the application of the clustering approach assists in reducing the amount of sent information, which increases the network lifetime [12][13]. A critical aspect of the proposed approach is represented by the need to perform advanced signal processing at the sensors, which entails significant energy consumption and makes the feature extraction mechanism essential to reduce energy consumption. Furthermore, the energy-aware design of systems solving complex problems requires efficient management of energy consumption without losing performance, which is carried out at a design level by solving the optimization problems involving energy consumption as a metric [12][14][15][16][17][18].
In the UWSN, the transmission process consumes more energy compared with sensing or computation processes. It consumes approximately 80% of the power for each sensor node [12].
Compared to terrestrial WSNs, underwater environments are characterized by unique features and face several issues, such as the depth-related impact on temperature, salinity, pressure, winds, and waves. These characteristics significantly affect the high-frequency waves used to collect sea-environment information (e.g., EM waves), which suffer from severe attenuation when used. Similarly, low-frequency signals, such as optical waves, need high-precision pointing beams, which suffer from scattering.
Underwater signal acquisition methods should have the capability to resist seawater characteristics. For an underwater medium, acoustic waves are less lossy and support long-range signal transmission. Thus, acoustic signals are primarily employed in underwater communication. Sound is a series of pressure perturbations that travel as a wave and exhibit phenomena such as reflection, diffraction, and interference [15]. Sonar sensors are considered an efficient choice because of their low fabrication cost and low power consumption. Moreover, sonar signals suffer less attenuation compared to other underwater techniques [16]. Developing a successful underwater mine detection system requires that mines can be distinguished (or classified) from other mine-like objects with great accuracy. Therefore, there is a solid need to extract the relevant information from the sonar data in order to evaluate and understand the signal properly. So-called feature extraction directly affects a system’s classification performance [19]. If the extracted features are not expressive for a certain problem, then the classification is not satisfactory [20]. At present, numerous techniques have been proposed for these subjects, including spectrogram correlation, time-frequency analysis, hidden Markov models, wavelet transformation (WT), and other approaches. The WT of signals has been widely employed for feature extraction. It converts the signals into a time or frequency domain, and the resultant wavelet coefficients can be used for classification [19]. Compared to the other feature extraction techniques—such as slop vector waveform, Fourier transforms, and chaos methods—WT consumes less energy, as it extracts the expressive information from the original signal.

2. Underwater Mines Detection by Sonar Signals 

2.1. Attainment of Submerged Signals and Feature Extraction

The effectiveness of the WT of sonar signals for feature extraction has been extensively investigated and studied. The research has shown that WT exceeds other techniques in providing accurate results.
WT has been employed as the activation function in certain deep learning models, and it has been proven to be successful [21]. To find the coefficient of a signal, the authors used a discrete Fourier transform and converted it to a sparse form, resulting in complex data. Complex-valued Haar and complex-valued Mexican hat are the wavelet activation functions used for the classification in the neural network. The researchers measured the accuracy of their work when running it with tenfold cross-validation (CV) and when splitting the data into 50% for training and 50% for testing. The accuracy values were 94.23% and 95.19%, respectively.
As an added bonus, a binary multistage classifier, which is a cascading series of classifiers that engage the Daubechies WT as the feature route, uses the extracted coefficients by using the Daubechies WT as the feature vector [22]. The authors tested their approach with wavelet levels ranging from two to thirty, using various extracted features. They were able to achieve an accuracy rate of 88%.
Battula et al. [23] proposed a data mining wavelet decision tree (WT tree) framework. It transforms sonar sounds using discrete Haar WT and then supplies the modified signals to the classifier for classification. As a first step, they used a learning procedure to identify the best features for classification. After that, they converted the features using Haar WT. This reduced the feature size and eliminated the misclassified characteristics. As a result, their categorization accuracy was correct 82.82% of the time.

2.2. Clustered UWASNs

Exploration has recently focused on the clustering mechanisms of underwater acoustic sensor nodes (UWASNs), owing to the critical importance of achieving the highest possible energy efficiency, given that underwater environments pose more significant challenges to preserving the sensor node battery [24][25][26].
UWSNs use a lot of energy, thus, scientists have recommended data aggregation and a round-based clustering strategy to cut down consumption [27]. The CH was chosen based on the following two factors: the node’s residual energy and the distance from the BS. Next, in the clustering formation, the CH sends an invitation message to all of its neighbors to join its cluster. In the end, the author used the Euclidean distance technique to see how similar the received and stored data were after receiving data from the cluster members and limiting transmissions. One of the two data packets was directed to the BS for further processing once a certain degree of similarity had been attained. According to the simulation’s outcomes, less energy was spent. This mechanism may lead to an energy imbalance between the nodes.
Three different communication paradigms are supported by the clustered routing system developed by Yadav et al. [15], which is built on three diverse communication paradigms, including the acoustic, free-space optical (FSO), and EM. The idea was developed for clustering using a calculated countenance to discover the ideal number of clusters. At the same time, the CH was selected based on the following three conditions: the residual energy of the node, the dynamic node deployment, and the heterogeneity of the nodes. According to the outcomes of their simulations, utilizing an acoustic communication paradigm instead of an FSO or an EM communication paradigm increased the network longevity the most. This mechanism reduces energy consumption but increases the end-to-end delay.
Hou [28] introduced a layered and clustered UWSN, where the network was divided into layers, with each layer containing one cluster. Its distance from the BS defines the size of a layer, in which closer layers have a lesser thickness than those that are farther away. When “hot spots” appeared, the concept of “layering” was established to manage the transmission between the nodes and the BS. It was determined that the candidate node’s residual energy, degree value with neighbors, and distance from the BS were all crucial factors in deciding which one was the CH. The simulation results showed that their framework extended the network’s lifetime better than a low-energy adaptive clustering hierarchy (LEACH) and a depth-and-energy-based clustered routing (DEBCR) algorithm.
Goyal et al. [29] proposed a fuzzy clustering algorithm based on the geographic information of the nodes for the cluster formation process and the size of the cluster. They selected the CH based on the following conditions: the distance from the cluster member to the candidate node, the node’s distance to the BS, and the energy required for the transmission. The simulation results demonstrated that their proposal decreased the percentage of a node’s death better than the LEACH protocol.
Having noted the problems with undersea routing, Ahmen et al. [30] investigated solutions to prolong the battery power of nodes and to control node mobility. Specifically, they recommended the clustered-based energy-efficient routing (CBE2R) protocol, which comprises standard sensor, source, courier, and sink nodes. This system splits the sea depth into seven strata, from the surface to the seabed. The authors also conducted empirical studies to prove these theoretical premises. In particular, they simulated CBE2R performance in contrast to conventional alternatives, such as the energy-efficient routing protocol (DRP), energy-efficient multipath grid-based geographic routing (EMGGR), and reliable and energy-efficient protocol (REEP). Their results prove the superior CBE2R performance, validating it as a solution for efficacious undersea routing. The limitations of the proposed approach are the void area and a fast consumption of courier nodes’ energy.
Finally, the authors in [31] employed a clustering technique to partition the network into several layers to resolve the challenge of power consumption in UWSNs using the clustering protocol. In their work, the CH was selected based on the residual energy of the candidate node and the transmission power needed to send data to the BS. The results revealed that their proposal avoided the early death of the distant CHs by routing the packet by multiple hops, rather than sending it directly to the BS. Compared to the Apple filing protocol (AFP) and DEBCR algorithm, the adaptive clustering routing algorithm for underwater acoustic sensor network (ACUN) in [31] consumed less energy overall. The limitation of this approach is that the competition process in CH election requires more message exchange.

2.3. Detecting Imagery Regarding Underwater Objects

Detecting and classifying sonar imagery concerning underwater objects represents a complicated challenge. High-resolution techniques have been used in several image-processing post-processing approaches to distinguish between the treated objects. Metal can be distinguished from other sub-bottom materials, such as rocks, by using a novel type of unconventional method detecting technique. Padmaja et al. [2] developed an innovative intruder detection system that relies on data mining and machine learning to identify submerged items, with 86% and 90% accuracy for a chosen feature set and a whole feature set, respectively.
It was discovered that deploying autonomous, unmanned vehicles to tow the sonar across the water was more cost-effective than the currently available human techniques. Using acoustic energy transmission at higher frequencies than human hearing, sonar, also known as ultrasonic sensing, is a technology used for obtaining environmental information [1]. Lower expenses, equality of (or higher) performance, and decreased operator deaths and injuries are some of the benefits of using autonomous vehicles, as reported by Khaledi et al. [1]. There were two current sonar alternatives and five distinct towing vehicles examined in the experiment. The underwater vehicle option used the least energy, according to the findings.
All of the available types of background removal are based on the premise that photometric scene characteristics display either temporal stationarity or are static in their behavior. The model fails when used to identify changes in scene dynamics rather than variations in the photometric qualities of the picture, as when trying to detect unusual patterns of automobile or pedestrian activity, for example. The scene dynamics are considered stationary in a new model and computational framework proposed by Jodoin et al. [5]. The method computes events by time-aggregating vector object descriptors with several characteristics. In this study, the researchers devised a novel algorithm that conducted temporal anomaly detection and localization quickly and efficiently. As a result, the current background subtraction approach is able to overcome this shortcoming.
Many factors can affect the classification and detection of underwater objects in sonar imagery, such as the environmental conditions, spatial clutter, the difference in target shapes, the fact that coral reefs may cover targets, and other factors. To cope with these challenges, the authors in [32] proposed a new method for detecting and classifying underwater objects in sonar imagery using canonical correlation analysis (CCA). CCA is efficient in extracting coherent features to enhance the classification and detection process, can distinguish between the return from the bottom of the water and objects, and can detect the activity of the target. Ultimately, CCA proves efficient in classifying and detecting underwater objects in sonar imagery and can reduce the false alarm rate.
The authors in [33] proposed a new algorithm for detecting submerged objects using synthetic aperture sonar (SAS). The algorithm merges highlight and shadow detection based on a weighted likelihood ratio test. The scheme’s primary advantage is detecting targets without any knowledge about their size or shape. Then, it uses a support vector machine (SVM) classifier to extract the statistical features of the pixels to detect the shadow in the regions of interest (ROIs). Finally, the authors proved the robustness of the proposed approach by comparing it with existing approaches.
One of the most significant UWSN technologies is localization, which is critical because it is employed in many applications. In Ref. [6], the authors classified localization algorithms into three categories according to the mobility of the sensor nodes, as follows: mobile localization algorithms, stationary localization algorithms, and hybrid localization algorithms. The detailed comparison of these localization algorithms has revealed existing knowledge gaps, such as the localization algorithms for hybrid and mobile UWSNs, which could lay the foundation for further research in this domain.
AI techniques were also used by Guo et al. in [34] for the detection, quantification, and visualization of dense microcracks in HPFRCC using a limited dataset of images with high-accuracy CNN-based model classification. This work shows the importance of using AI detection techniques for accurate classification. This statement was also confirmed by Liu et al. in [35], where the authors have shown the efficiency of applying machine learning algorithms for the efficient detection of anomalies.
Using image-based sensing for underwater object detection requires high energy consumption in the processing of data for feature extraction and classification.

2.4. Energy Resource Management in UWSNs

Void node avoidance algorithms represent a crucial strategy for energy-efficient resource management in the energy-constrained UWSNs. Javaid et al., in [8], proposed AVN-AHH-VBF and CoAVN-AHH-VBF as two different UWSN routing protocols, with one based on collaboration (CoAVN-AHH) and the other based on ad hoc vector-based adaptability (VBA). Both models employed sensor nodes to forward data packets, but the strategies used to keep the network from flooding differed in each model [8]. However, compared to the existing void node avoidance methods, these suggested methodologies significantly improved the network performance. The limitation of this proposed approach is that it is not flexible enough when the nodes follow an irregular distribution.
To optimize the available resources and prolong the network lifespan, Sher et al. [36] proposed the following four systems: collision-avoidance-based WDFAD-DBR (CA-DBR), backward-transmission-based WDFAD-DBR (B-DBR), cluster-based WDFAD-DBR (C-DBR), and WDFAD- depth-based routing (DBR) and (A-BDR). The C-DBR creates small groups of nodes to collect data, limiting the end-to-end delay. Contrarily, the A-DBR averts void nodes by altering the transmission range adaptively. The B-DBR finds an alternative data packet route delivery, while the CA-DBR minimizes collision. Simulations of the four systems display superiority to the baseline alternatives regarding the accrued propagation distance, end-to-end delay, energy tax, and average packet delivery ratio. In brief, jointly deploying the four schemes facilitates void hole avoidance, enabling reliable data transfer. The limitations of the DBR approach are void holes, increased energy consumption, and high end-to-end delay.
Considering the need for efficient packet transmission, Chaaf et al. [37] proposed the relay-based void hole prevention and repair protocol (ReVOHPR). This strategy is highly effective for locating and avoiding trap relay nodes and void holes. In addition, the protocol employs several cutting-edge technologies to make sure that it works even while submerged. It is easier to transport traffic between clusters when there are as many matching nodes as possible. Bi-criteria mayfly optimization may also locate and fix void holes in a given structure, which is an added benefit. The authors also simulated ReVOHPR’s performance and found that it outperformed the baseline traditional approaches by a wide margin. Because of this, the void hole issue is no longer a concern when using ReVOHPR. Void holes still exist and are an overhead, due to control packets’ exchange.
The huge size and restricted communication radius characterize a wireless sensor network. The effective delivery of a data packet and a pattern of nodes is largely dependent on multi-hop transmissions [13]. While approximating the forwarding multi-hop paths quality is imperative, existing metrics, for instance, ETX, overlook the forwarding capabilities inside the sensor nodes and concentrate on gauging the link performance between the nodes. The researchers in [13] proposed quality of forwarding (QoF) to fill the knowledge gap left by previous studies. QoF assesses the performance in the gray zone inside a node, and the measurements of the intact path quality support the designing of efficient multi-hop routing protocols. The study outcomes have revealed that the developed modified collection tree protocol considered both forwarding reliability and transmission cost, resulting in high throughput for data collection.
Additionally, Ismail and Bchir [16] proposed a new approach for automatic mine detection in sonar data, which relies on a possibilistic-based fusion technique to categorize sonar incidents as mine-like or mine objects. This approach produced optimal fusion parameters for every setting, and the outcomes proved that it outperformed unsupervised local fusion and individual classifiers.
Finally, the literature analysis shows how far UWSN development has come in the last several years. Researchers have presented feasible solutions for several challenges, including void holes, the limited availability of battery power, and uncontrolled node mobility. However, the techniques shown here are an excellent starting point for creating an energy-aware framework for detecting underwater mines.

This entry is adapted from the peer-reviewed paper 10.3390/electronics12224598

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