Underwater energy consumption and target localization balancing driven by network coverage optimization

Aiming at the issue that in view of the common underwater target electric field detection and localization technology, its electric signal attenuation in the water is faster and easy to be affected by the underwater complex environmental noise, which affects the detection performance of the problem, the paper carries out a research based on active and passive electric field detection and localization technology. First, a target electric field detection and localization system is designed. Then, two sets of basis functions in the horizontal and vertical directions are derived based on this detection system model, and then the detection and localization of underwater targets are realized by energy calculation. Finally, the image fusion algorithm of wavelet transform is adopted to further realize the detection and localization of underwater targets in a two-dimensional plane through the form of a heatmap. All the above contents are experimentally verified by simulation tools, and the experimental results show that compared with the traditional electric field detection and localization techniques for underwater targets, the method in this paper has better performance in the environment with a lower signal-to-noise ratio.

A key component of wireless sensor networks (WSN) is optimal coverage. For WSN to have network lifetime and optimal resource utilization, maximum coverage must be guaranteed. The unequal distribution of sensor nodes in densely populated regions contributes to the build-up of network coverage. This research suggests a revolutionary intelligent deep learning-based optimization approach for WSN coverage optimization to tackle this problem. Here, the optimal network coverage of WSN is performed by the novel enhanced deep Q-network (EDQN) algorithm, where the parameters of DQN are tuned by nature inspired optimization algorithm called hippopotamus optimization (HO) with the intention of attaining the fitness function. In order to maximize coverage, the node position is changed in the developed methodology to represent the spatial properties of the network. Along with lowering latency, the increased coverage also increases throughput and network longevity. The outcome of several network coverage optimization trials is computed, and the EDQN-HO’s impact on network coverage optimization is also determined by adjusting the parameters. The network coverage optimization studies’ simulated findings demonstrate that the suggested EDQN-HO may be effectively utilized in a variety of settings. The proposed EDQN-HO for the WSN coverage model returns superior outcomes with 18.31%, 78.95%, 10.65%, 87.5%, 83.33%, and 38.20% than the existing methods in terms of coverage rate, energy consumption, computing time, positioning error, network lifetime, and average moving distance respectively.

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions. This paper presents a novel approach to employing RNNs to increase the accuracy of underwater target localization by exploiting the temporal dynamics of proximity-informed data. This method uses an RNN architecture to track changes in audio emissions from underwater targets sensed by a microphone network. Using the temporal correlations represented in the data, the RNN learns patterns indicative of target localization quickly and correctly. Furthermore, the addition of proximity-based features increases the model's ability to understand the relative distances between hydrophone nodes and the target, resulting in more accurate localization estimates. To evaluate the suggested methodology, thorough simulations and practical experiments were carried out in a variety of underwater environments. The results show that the RNN-based strategy beats conventional methods and works effectively even in difficult settings. The utility of the proximity-aware RNN model is demonstrated, in particular, by considerable reductions in the mean estimate error (MEE), an important performance measure.

Underwater imaging plays an important role in sonar detection. Compressive sensing can achieve high-precision airborne sound source localization with a small computational effort, but underwater target localization and ranging have not been fully achieved. This study proposes a metamaterial-based compressive sensing imaging method for underwater sound source localization and target imaging. To overcome the poor wave manipulation performance caused by the impedance difference between water and the structure, a strong coupling interaction between the flexible structure and the medium is introduced to reduce reflection and scattering, dramatically improving the acoustic signal transmission amplitude via the resonance mechanism. Analytical and numerical results show that this device can achieve directional irrelevant modulation of incident acoustic waves in different directions. Besides, the multi-information fusion compressive sensing method and dual-sensor joint compressive sensing method can reconstruct the sound source information from the received mixed signals. Experimental results show that the metamaterial-based compressive sensing imaging device realizes the perception and localization of a single sound source underwater in multiple directions. The metamaterial-based hydroacoustic imaging device eliminates the dependence on large-caliber arrays and complex control systems, thus opening the way to establish a sonar system with low cost and high precision.

Considering the limited availability of usable signal sources and the instability of sensor measurements in underwater environments, this paper proposes a novel underwater target enhanced localization using distributed estimation and search optimization. First, a dual mapping from the signal domain to the position domain is established using time-of-arrival measurements between acoustic base stations and underwater beacons. Second, a singular value decomposition-enhanced unscented Kalman filter is introduced to improve the stability of the covariance matrix. Combined with a strong tracking mechanism, the filter gain is adaptively adjusted to perform a first-stage filtering estimation. Then, the underwater target localization optimization is transformed into an unconstrained problem, where a cascade quasi-Newton optimization model is constructed to mitigate the accumulation of errors and suppress abnormal positioning outputs. Compared with the improved two-step weighted least squares, the square-root unscented Kalman filter, and the successive constrained optimization modified polar representation, the proposed method demonstrates superior localization accuracy in both multivariable simulations and lake-based field experiments. The consistency between simulation and experimental results confirms the effectiveness of the proposed method in suppressing the impact of uncertain measurements.

Aiming at the application demand in underwater noise monitoring, observation of marine animal, antisubmarine and underwater target localization, a high-SNR underwater acoustic signal acquisition (UASA) node that combines a self-contained acquisition system and floating platform is designed to improve the acquisition performance of a single UASA node, and a high-accuracy synchronization sampling method among multiple distributed UASA nodes based on master-slave dual phase-locked loops (MSDPLL) is proposed to improve the synchronization sampling accuracy. According to the equivalent model of hydrophone and application requirements, low noise signal conditioning circuit and large-capacity data storage modules are designed. Based on the long-term monitoring requirements for underwater acoustic signal and distributed positioning requirements for underwater targets, the structure of a single UASA node is designed and MSDPLL is developed for high-accuracy synchronization sampling among multiple UASA nodes. Related experimental results verified the performance of the UASA node and the synchronization sampling method.

A real-time magnetic localization method based on unscented particle filter (UPF) is proposed to solve the problem for real-time localization of underwater non-cooperative magnetic targets. The state space model of non-cooperative magnetic targets is set up according to its motion characteristic, and the state is estimated in real time by UPF. In order to improve the observability of the system, the particle state is constrained and magnetic moment is inverted by the least square method. The simulation results and ferromagnetic object localization experimental results show that the method is good in localization precision and real-time performance, which can be applied to the real-time magnetic localization in near field.

Active sonar systems are used in the detection and localization of underwater targets. While traditional approaches use pulsed active sonar (PAS) to transmit short bursts, high duty cycle (HDC) sonar systems have been the focus of recent research and can overcome the shortcomings of PAS. Since HDC sonar systems transmit a long pulse train waveform, we must address sub-pulse interference issues, which requires multiple orthogonal sub-pulses and generalized sinusoidal frequency modulated (GSFM) pulses are suitable for this purpose. Unfortunately, conventional GSFM pulse train design methods do not generate an adequate number of orthogonal sub-pulses. Therefore, we propose an improved GSFM pulse train waveform design approach for overcoming the limitations of these conventional methods. The proposed method is organized into two parts, the first focused on the assessment of the auto-correlation, and the second focused on the optimizing the orthogonality between sub-pulses. Cost function is also carefully designed for this purpose. From simulation experiments, we found that the proposed method was able to produce 30 orthogonal sub-pulses and an optimized waveform that demonstrated better detection performance than conventional waveforms under most conditions. We also verified the performance of the proposed method via actual sea experiments, with analysis showing superior performance matching that of simulations. An analysis on the bandwidth parameter K is also conducted and it is found K = 1.25 to be the best option.

Wireless sensor network (WSN) coverage optimization is a critical factor in improving network service quality, yet it faces challenges such as deployment uniformity, high-dimensional optimization, and the balance between exploration and exploitation under limited node resources. To address the shortcomings of the cultural historical optimization algorithm (CHOA), including insufficient global exploration, lack of dynamic regulation, and limited local exploitation accuracy, this paper proposes a film and television strategy-based multi-strategy cultural historical optimization algorithm (FTSCHOA). The proposed algorithm enhances performance through three synergistic mechanisms: a DE-style evolutionary operator that strengthens global exploration and population diversity; a film-and-television strategy that balances exploration and exploitation via random perturbations and adaptive parameter regulation; and a memory-based neighborhood local search that performs refined exploitation around high-quality solution sets to improve local optimization accuracy. Extensive experiments conducted on the CEC2017 and CEC2022 benchmark suites with dimensions of 10, 20, 30, and 50 demonstrate that FTSCHOA outperforms comparative algorithms in terms of optimization accuracy, convergence speed, and stability. The Friedman mean rank test indicates that FTSCHOA consistently achieves the best average ranking, while the Wilcoxon rank-sum test confirms that its performance differences with respect to competing algorithms are statistically significant (p<0.05). When applied to WSN coverage optimization in a 100 m×100 m monitoring region, FTSCHOA achieves coverage rates of 0.9351 and 0.9738 with 25 and 30 sensor nodes, respectively, which are significantly higher than those obtained by PSO, GWO, CHOA, and other algorithms. Moreover, the resulting node deployments exhibit greater uniformity, fewer coverage holes, and lower redundancy. The experimental results demonstrate that FTSCHOA effectively overcomes the limitations of traditional algorithms and provides an efficient and practical solution for WSN node deployment optimization, with strong potential for application in real-world scenarios such as environmental monitoring and smart agriculture.

The work examines perspectives from the inclusion of the autonomicity and self-manageability features in the scope of Future Internet's (FI) deployment. Apart from the strategic importance for further evolution, we also discuss some major future challenges among which is the option for an effective network management (NM), as FI should possess a considerably enhanced network manageability capability. We examine a new network manageability paradigm that allows network elements (NEs) to: be autonomously interrelated/ controlled; be dynamically adapted to changing environments, and; learn the desired behaviour over time, based on the original context of the Self-NET research project effort. As self-organizing and self-managing systems have a considerable market impact, we identify benefits for all market actors involved. In addition, we incorporate some recent, but very promising experimental findings, mainly based on the context of a specific use-case for network coverage and capacity optimization, highlighting the way towards developing specific NM-related solutions, able to be adopted by the real market sector. We conclude with some essential arising issues.

This paper presents results on recent developments pertaining to the coordinated motion control of a fleet of marine robotic vehicles. Specifically, we address the Cooperative Moving Path Following (CMPF) motion control problem, that consists of steering the robotic vehicles along a priori specified geometric paths that jointly move according to a target frame, while achieving a pre-defined coordination objective. To this end, each vehicle will need to communicate with their neighbors in order to cooperatively solve the CMPF task. Two distinct robust Moving Path Following motion control strategies for achieving robustness on the moving path following tasks are proposed. Experimental results demonstrating the application of CMPF to marine vehicles in the context of source localization and tracking of underwater targets are presented backed with stability and convergence guarantees.

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.

The 2020 climate and energy package adopted in 2009 sets three key targets for EU Member States: 20% cut in greenhouse gas emissions (from 1990 levels); to ensure 20% of renewables in final energy consumption and to achieve 20% improvement in energy efficiency from 2005 levels. The paper analyses the progress of selected countries in achieving climate and energy targets by 2020. Baltic States, namely Lithuania, Latvia, and Estonia were selected for comparative assessment of their achievements in GHG emission reduction, energy efficiency improvement and use of renewable energy sources. Indicator’s framework was constructed to capture the most important drivers of GHG emission reduction from fuel combustion. These indicators allow to compare countries in their achievements by assessing their main energy and climate change policies. The analysis and comparison of the trends of the main indicators allows to define the best performing country and the reason of this. Policy implications were developed for Baltic States to pursue further progress in achieving energy and climate targets for 2030 and 2050.

The underwater electric field signal can be excited by underwater vehicles, such as the shaft-rate electric field and the corrosion electric field. The electric field signature of each vehicle exhibits significant differences in time and frequency domain, which can be exploited to determine target positions. In this paper, a novel passive localization method for underwater targets is presented, leveraging a uniform linear electrode array (ULEA). The ULEA manifold along the axial direction is derived from the electric field propagation in an infinite lossy medium, which provides the nonlinear mapping relationship between the target position and the voltage data acquired by the ULEA. In order to locate the targets, the multiple signal classification (MUSIC) algorithm is applied. Then, capitalizing on the rotational invariance of matrix operations and exploiting the symmetry inherent in the ULEA, we streamline the six-dimensional spatial spectral scanning onto a two-dimensional plane, providing azimuth and distance information for the targets. This method significantly reduces computational overhead. To validate the efficacy of our proposed method, we devise a localization system and conduct a simulation environment to estimate targets. Results show that our method achieves satisfactory direction and reliable distance estimations, even in scenarios with low signal-to-noise ratios.

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