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Unmanned aerial vehicles (UAVs) provide efficient and flexible means for maritime emergency rescue, with path planning being a critical technology in this context. Most existing unmanned device research focuses on land-based path planning in two-dimensional planes, which fails to fully leverage the aerial advantages of UAVs and does not accurately describe offshore environments. Therefore, this paper establishes a three-dimensional offshore environmental model. The UAV's path in this environment is achieved through a novel swarm intelligence algorithm, which is based on the coati optimization algorithm (COA). New strategies are introduced to address potential issues within the COA, thereby solving the problem of UAV path planning in complex offshore environments. The proposed OCLCOA introduces a dynamic opposition-based search to address the population separation problem in the COA and incorporates a covariance search strategy to enhance its exploitation capabilities. To simulate the actual environment as closely as possible, the environmental model established in this paper considers three environmental factors: offshore flight-restricted area, island terrain, and sea winds. A corresponding cost function is designed to evaluate the path length and path deflection and quantify the impact of these three environmental factors on the UAV. Experimental results verify that the proposed algorithm effectively solves the UAV path planning problem in offshore environments. [ABSTRACT FROM AUTHOR]

Due to disparities in tolerance, resource availability, and acquisition of labeled training data between satellite-terrestrial integrated networks (STINs) and terrestrial networks, the application of traditional terrestrial network intrusion detection techniques to satellite networks poses significant challenges. This paper presents a satellite network intrusion detection system named Bi-LSTM sparse selfencoder (BLSAE-SNIDS) to address this issue. Through the development of an innovative unsupervised training Bi-LSTM stacked self-encoder, BLSAE-SNIDS facilitates feature extraction from satellite network traffic, diminishes dimensionality, considerably reduces training and testing durations, and enhances the attack prediction accuracy of the classifier. To assess the efficacy of the proposed model, we conduct comprehensive experiments utilizing STIN and UNSW-NB15 datasets. The results obtained from the STIN dataset demonstrate that BLSAE-SNIDS achieves 99.99% accuracy with reduced computational and transmission overheads alongside enhanced flexibility. Furthermore, results from the UNSW-NB15 dataset exhibit BLSAE-SNIDS’ proficiency in detecting various network intrusion attacks efficiently. These findings indicate that BLSAE-SNIDS suits general satellite security networks and offers a novel approach to designing security systems for polar satellite networks, thus exhibiting practical utility. [ABSTRACT FROM AUTHOR]

A well-performing data-driven sparse sensor deployment strategy is critical for marine monitoring systems, as it enables the optimal reconstruction of marine physical quantities with fewer sensors. However, ocean data typically contain substantial amounts of noise, including outliers (incomplete data) and inherent measurement noise, which heightens the complexity of sensor deployment. Therefore, this study optimizes the sparse sensor placement model by establishing noise indicators, including small noise weight and large noise weight, which are measured by entropy to minimize the prediction bias. Building on this, a robust sparse sensor placement algorithm is proposed, which utilizes the block coordinate update (BCU) iteration method to solve the function. During the iterative updating process, the proposed algorithm simultaneously updates the selection matrix, reconstruction matrix, and noise matrix. This allows for effective identification and mitigation of noise in the data through evaluation. Consequently, the deployed sensors achieve superior reconstruction performance compared to other deployment methods that do not incorporate noise evaluation. Experiments are also conducted on datasets of sea surface temperature (SST) and global ocean salinity, which demonstrate that our strategy significantly outperforms several other considered methods in terms of reconstruction accuracy while enabling autonomous sensor deployment under noisy conditions. [ABSTRACT FROM AUTHOR]

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably. [ABSTRACT FROM AUTHOR]

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions. [ABSTRACT FROM AUTHOR]