At present, China's maritime security is facing two major challenges: the deterioration of the environment has led to a significant reduction in the area of islands and reefs, threatening territorial security; and the strict monitoring of strait passages has hindered the deployment of underwater forces. Unmanned underwater vehicles are the core equipment for marine ecological protection and national security maintenance. However, existing unmanned underwater vehicles are unable to meet multiple requirements simultaneously: Propeller-driven underwater vehicles have high speed and maneuverability, but they cause significant disturbance to organisms, lack sufficient concealment, and are unable to accurately obtain ecological information or effectively respond to hostile control on sensitive passages; Underwater gliders have good range and concealment, but their maneuverability is weak, and they cannot meet the requirements of complex tasks. It is urgent to develop biomimetic underwater vehicles that are biocompatible, quiet and concealed, have long-term self-sustainability, and can perform coordinated operations. Among them, the manta ray-inspired underwater vehicle adopts the mode of using its wide pectoral fins to achieve bowed gliding and alternating flapping movements, which performs outstandingly in terms of gliding efficiency, flapping maneuverability and motion stability, and is an ideal biomimetic prototype. This work breaks through the limitations of previous studies, which mostly focused on a single motion mode. For the first time, it systematically reviewed the multi-modal motion hydrodynamic mechanisms of the the manta ray-inspired underwater vehicle from the individual to the cluster level, integrating various motion forms such as bowed gliding, continuous flapping, alternating gliding and flapping, and isomorphic/heteromorphic clusters into the same review framework. The study focused on analyzing the research progress in three key aspects: morphology and motion modeling methods, the efficient propulsion mechanism of the individual, and the coupling mechanism of the cluster flow field. In terms of modeling, key data such as the skeletal structure, shape parameters, and kinematic characteristics of the manta ray were selected, and the flapping mode, skeletal distribution, and kinematic laws of the pectoral fins were systematically revealed. In terms of single-body propulsion, the core mechanism of improving the lateral variation of the flow line of the pectoral fins to achieve drag reduction through arched gliding and the key role of the chordal deformation of the pectoral fins in generating thrust were clarified. In terms of the cluster, research was conducted around factors such as the number of clusters, formation, spacing, and propulsion mode, and it was determined that the fusion and collision of the wake was the fundamental reason for the differences in hydrodynamic performance among individual organisms. Based on this, a "modeling - mechanism - performance" research framework was initially formed, providing a theoretical basis for bionic design and optimization. However, breakthroughs are still needed in aspects such as model fidelity, non-stationary and complex environment mechanisms, and the transformation from theory to design. High-fidelity simulation models including real attachment structures should be developed. The research scope should be expanded to complex environments such as cross-media entry and exit from water, expanding the operational boundaries and task capabilities of the the manta ray-inspired underwater vehicle. The hydrodynamic mechanism in dynamic clusters should be explored, and research methods integrating artificial intelligence and autonomous swimming simulation should be developed to achieve overall hydrodynamic performance optimization during formation transformation and multimodal conversion processes. All of the above will promote the collaborative optimization of the configuration and motion strategies of the vehicle, enabling it to achieve a dynamic balance among high efficiency, high maneuverability and strong stability in complex and realistic marine environments and diverse mission scenarios. This will lay an irreplaceable hydrodynamic foundation for the application of the manta ray-inspired underwater vehicle in deep and remote seas.
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Wireless sensor networks (WSNs) are essential for ocean monitoring and are widely used in environmental monitoring, target localization, marine resource development, and disaster warning applications. However, WSNs often face challenges such as arbitrary deployment strategies, low effective coverage, and high coverage redundancy, which degrade network performance. To address these issues, this paper proposes a fractional-order chameleon swarm algorithm (FCSA) to optimize the deployment of static WSN nodes.
First, an improved Circle chaotic mapping method is employed to enhance population diversity and global distribution, ensuring higher-quality initial conditions for optimization. Next, during the velocity update phase, a fractional-order velocity update strategy is introduced to effectively leverage the historical search experiences of individuals, enhancing the balance between global exploration and local exploitation. Furthermore, the Levy flight mechanism is incorporated into position updates, providing stronger jumping characteristics and adaptability. These improvements enable FCSA to effectively optimize key performance indicators such as coverage rate and coverage redundancy, significantly enhancing deployment efficiency and distribution uniformity for static WSN nodes while ensuring better adaptability to complex environments.
Simulation results demonstrate that FCSA outperforms CSA, CSA-Circle, CSA-Levy, GA, RSO, and eleven other classical optimization algorithms in static node deployment. FCSA achieves a high coverage rate of 0.8018 while significantly reducing coverage redundancy to 0.0078. Additionally, for single optimization tasks, FCSA exhibits the fastest convergence, requiring only 638 iterations to reach a fitness value of 0.191409, significantly outperforming other algorithms. After 30 independent runs, statistical analysis shows that FCSA maintains an extremely fast convergence speed in the early iterations, reaching an optimal fitness value of 0.198222 after 1000 iterations. Among the ten algorithms, FCSA is the only one with a standard deviation of the fitness value below 0.2, indicating superior global search ability, higher convergence accuracy, and better distribution uniformity. It effectively mitigates the issue of uneven node distribution observed in traditional algorithms while maintaining strong stability and robustness.
In addressing the WSN static node deployment problem, FCSA effectively optimizes sensor placement, significantly improving monitoring quality through a multi-strategy collaborative optimization approach. The algorithm exhibits strong robustness and adaptability in complex environments. Additionally, FCSA provides an efficient, high-quality deployment solution for ocean monitoring and similar applications, offering strong theoretical and technical support for sensor network optimization and expansion, with significant application potential and practical value.
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