Search Results (181)

Minimizing energy consumption remains a critical challenge in wireless sensor networks (WSNs) because of their reliance on nonrechargeable batteries. Clustering-based hierarchical communication has been widely adopted to address this issue by improving residual energy and balancing the network load. In this architecture, cluster heads (CHs) are responsible for data collection, aggregation, and forwarding, making their optimal selection essential for prolonging network lifetime. The effectiveness of CH selection is highly dependent on the choice of metaheuristic optimization method and the design of the fitness function. Although numerous studies have applied metaheuristic algorithms with suitably designed fitness functions to tackle the CH selection problem, many existing approaches fail to fully capture both the spatial distribution of nodes and dynamic energy conditions. To address these limitations, we propose the binary secretary bird optimization clustering (BSBOC) method. BSBOC introduces a binary variant of the secretary bird optimization algorithm (SBOA) to handle the discrete nature of CH selection. Additionally, it defines a novel multiobjective fitness function that, for the first time, considers the Voronoi diagram of CHs as an optimization objective, besides other well-known objectives. BSBOC was thoroughly assessed via comprehensive simulation experiments, benchmarked against two advanced methods (MOBGWO and WAOA), under both homogeneous and heterogeneous network models across two deployment scenarios. Findings from these simulations demonstrated that BSBOC notably decreased energy usage and prolonged network lifetime, highlighting its effectiveness as a reliable method for energy-aware clustering in WSNs. Full article

The k-nearest- neighbor (kNN) algorithm is crucial in data mining and machine learning, yet its deployment on large-scale datasets within cloud environments presents significant security and efficiency challenges. This paper is dedicated to advancing the resolution of these challenges and presents novel contributions to the development of efficient and secure exact kNN query schemes tailored for spatial datasets in cloud-based location services. Addressing existing limitations, our approach focuses on accelerating query processing while ensuring robust privacy preservation and public verifiability. Key contributions include the establishment of a formal framework underpinned by stringent security definitions, providing a solid groundwork for future advancements. Leveraging Paillier’s homomorphic cryptosystem and public-key signature techniques, our design achieves heightened security by safeguarding databases, query access patterns, and result integrity while enabling public verification. Additionally, our scheme enhances computational efficiency through optimized data-packing techniques and simplified Voronoi diagram-based ciphertext index construction, leading to substantial savings in computational and communication overheads. Rigorous and transparent theoretical analysis substantiates the correctness, security, and efficiency of our design, while comprehensive experimental evaluations confirm the effectiveness of our approach, showcasing its practical applicability and scalability across datasets of varying scales. Full article

A main challenge in deploying wireless sensor networks (WSNs) is determining the minimum quantity of sensor nodes required to fully cover the region of interest while avoiding coverage holes. This study proposes a method to compute the number of nodes needed to monitor a circular region and a distributed control strategy based on circular formations to move dynamic agents to their desired positions. The method addresses the coverage problem, ensuring that each point in the monitored region is detected without losing connectivity. In addition, the study compares this approach with a sensor node allocation method based on Voronoi diagrams, highlighting the need for an algorithm that computes the desired positions of the agents to provide guaranteed flawless coverage; the proposed method achieves this by obtaining the desired final positions. The hybrid WSN architecture, together with the proposed method, achieves full coverage efficiently and better utilizes the detection circumference of sensors compared to traditional rectangular monitoring regions. Full article

Gradient porous structures (GPS) offer significant mechanical and functional advantages over homogeneous counterparts. This paper proposes a computational design framework utilizing spatial Voronoi diagrams to create lightweight, stress-responsive spatial frames optimized for architectural double-curvature arched shell roofing components. The method integrates Solid Isotropic Material with Penalization (SIMP)-based topology optimization (TO) to establish initial stress-informed material distributions, adaptive Voronoi control point (CP) placement guided by localized stress data, and a bi-objective genetic algorithm (GA) optimizing maximum and average displacement. Following optimization, a weighted Lloyd relaxation (LR) refines Voronoi cells into spatial frameworks with varying densities corresponding to stress gradients. Finite Element Analysis (FEA) demonstrates that the optimized Voronoi-driven GPS achieves notable improvements, revealing up to 79.7% material volume reduction and significant improvement in structural efficiency, achieving a stiffness-to-weight ratio (SWR) exceeding 2200 in optimized configurations. Furthermore, optimized structures consistently maintain maximum von Mises (MVM) stresses below 20 MPa, well within the allowable yield strength of the Polyethylene Terephthalate Glycol (PETG) material (53 MPa). The developed framework effectively bridges structural performance, material efficiency, and aesthetic considerations, offering substantial potential for application in advanced, high-performance architectural systems. Full article

The issue of energy transfer is extremely important. In order to achieve the lowest possible energy consumption and the required thermal efficiency in energy-efficient buildings, it is necessary, among other things, to minimize the heat-transfer coefficient, which depends on the properties of the insulating material. Analyses of the relationship between the structure of a material and its thermal conductivity coefficient have shown that lower values of this coefficient can be achieved with a more complex structure that mimics natural forms. This paper presents a design method based on the Voronoi diagram to obtain a three-dimensional structure of a porous composite material. The method was found to be effective in producing structures with predefined and functionally graded porosity. The porous specimens were fabricated from a biodegradable soybean oil-based resin using mSLA additive technology. Analyses were performed to determine the thermal parameters of the anisotropic composites. Experimental results showed that both porosity and irregularity affect the thermal properties. The lowest thermal conductivity coefficients were obtained for a 100 mm-thick prototype composite with the following parameters: wall thickness D = 0.2 mm, cell size S = 4 mm, number of structural layers n = 2, and degree of irregularity R = 4. The lowest possible thermal conductivity of the insulation was 0.026 W/(m·K), and the highest possible thermal resistance was 3.92 (m²·K)/W. The method presented in this study provides an effective solution for nature-inspired design and topological optimization of porous structures. Full article

This paper presents a theoretical framework for constructing generalized weighted Voronoi diagrams (GWVDs) of weighted points and straight-line segments (“sites”) in the Euclidean plane, based on lower envelopes constructed in three-dimensional space. Central to our approach is an algebraic distance function that defines the minimum weighted distance from a point to a site. We also introduce a parameterization for the bisectors, ensuring a precise representation of Voronoi edges. The connection to lower envelopes allows us to derive (almost tight) bounds on the combinatorial complexity of a GWVD. We conclude with a short discussion of implementation strategies, ranging from leveraging computational geometry libraries to employing graphics hardware for approximate solutions. Full article

When two axisymmetric stress waves of the same strength propagate radially at the same velocity, the stress wave wavefronts collide and interact along a specific locus, which is the perpendicular bisector between the two sources. The maximum principal stress occurs along this perpendicular bisector, and the tensile stresses result in crack bridging between the two source points. This symmetric wave propagation behavior allows us to use the Voronoi tessellation diagram and its symmetric dual graph, the Delaunay triangulation, to gain first-order insight into complex wave propagation phenomena for an arbitrary distribution of wave propagation sources. The inherent symmetry of these simultaneous wave propagation mechanics allows us to rapidly visualize and predict the stress wave propagation and interactions, and the resultant crack bridging patterns that arise from random blast sources. The current work is focused on rock blast fracture mechanics, but the visualization scheme can be implemented for any application where waves propagate axisymmetrically and interact. Full article

Polyurethane foams are frequently used to provide thermal insulation. Thanks to the blowing agents used during their synthesis, pores are created in the structure and thermal insulation is achieved through these pores. In this study, five different insulating polyurethane foam samples containing water and cyclohexane blowing agents were synthesized. Pore stabilities and their effects on pore neighboring were analyzed computationally (MP2/aug-cc-pVDZ). A digital image processing- and machine learning-based algorithm was developed to predict the mean neighboring effect distances of the produced foams. It was created using the Voronoi tessellation method used for the identification problems in industrial applications. This method showed that there was a close relationship between the calculated Voronoi neighboring effect distances of the samples and their thermal conductivity coefficients. Considering the Voronoi neighboring effect distances proposed in this study, the thermal conductivity coefficient of similar polyurethane foams could be predicted. This method required only a standard mobile phone to capture images of the samples and the algorithm developed using Python (version 3.13.2) programming language. In addition, when compared to the local surface imaging device SEM, it allowed the entire surface to be analyzed faster and at once, without any surface deterioration. Full article

CoCrNi medium-entropy alloy (MEA) coatings prepared using laser cladding (LC) with unique properties have aroused great interest in recent years and have been widely studied. However, limited studies have been conducted on the effect of adding Al/Fe on the wear properties of CoCrNi MEA coatings prepared on the surface of stainless steel. In this study, AlCoCrFeNi, CoCrFeNi, and CoCrNi MEA LC coatings were prepared on a stainless steel substrate. The grain structures and microscopic morphologies of coatings were characterized, and the wear mechanisms were analyzed using the nano-indentation and wear tests. The hardness-strengthening mechanism was theoretically investigated using phase diagrams and molecular dynamics (MD). The phase diagram results show that the addition of Al lowered the nucleation initiation temperature, thereby increasing the nucleation rate and forming more grains. Moreover, according to the Voronoi volumes and mean–square atomic displacements (MASD) results using MD, the addition of Al makes the appearance of severe localized lattice distortions, while the addition of Fe tends to form short-range ordered structures. In summary, fine-grain strengthening and the hardness strengthening caused by local lattice distortion were the main strengthening mechanisms of AlCoCrFeNi. These findings are highly significant for expanding the application potential and provide profound insights into the wear properties of the CoCrNi MEA coatings. Full article

We investigated patterns formed during the polymerization process of bifunctional monomers in a liquid crystal for both large polymer concentrations (polymer-dispersed liquid crystals, PDLC) and small concentrations (polymer-stabilized liquid crystals, PSLC). The resulting experimental patterns are reminiscent of Voronoi diagrams, so a reverse Voronoi algorithm was developed that provides the seed locations of cells, thus allowing a computational reproduction of the experimental patterns. Several metrics were developed to quantify the commonality between the faithful experimental patterns and the idealized and generated ones. This led to descriptions of the experimental patterns with accuracies better than 90% and showed that the curvature or concavity of the cell edges was below 2%. Possible reasons for the discrepancies between the original and generated Voronoi diagrams are discussed. The introduced algorithm and quantification of the patterns could be transferred to many other experimental problems, for example, melting of thin polymer films, ultra-thin metal films, or bio-membranes. The discrepancies between the experimental and ideal Voronoi diagrams are quantified, which may be useful in the quality control of privacy windows, reflective displays, or smart glass. Full article

Natural disasters often occur unexpectedly, catching people off guard, such as the Hualien earthquake on 3 April 2024. Many of the evacuation-monitoring systems currently in place lack real-time updates of shelter capacities, which raises the risk of overcrowding under wartime scenarios. This study developed a system for the targeted assignment of evacuation sites during air raids. The DBSCAN algorithm was used to group data based on pedestrian flow patterns and an LSTM model was used to enhance the prediction speed and accuracy. Weighted Voronoi diagrams delineated regions to identify optimal evacuation points, while real-time SMS notifications through base station positioning disseminated evacuation information to the public. The experiment results demonstrated the effectiveness of the proposed system in facilitating safe evacuations across a broad range of geographic regions while reducing the number of LSTM models. Dynamic updates on the shelter capacities make it possible for citizens to make informed decisions during air raid emergencies. Full article

With the widespread application of unmanned aerial vehicles (UAV) in surveying, disaster search and rescue, agricultural spraying, war reconnaissance, and other fields, coverage path planning is one of the most important problems to be explored. In this paper, a large-area coverage path planning (CCP) method based on vehicle–UAV collaboration is proposed. The core idea of the proposed method is adopting a divide-and conquer-strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ant colony optimization algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag CCP method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle–UAV collaboration CCP method, and two comparative experiments demonstrate that the proposed method excels at large-scale CCP scenarios, and achieves a significant improvement in coverage efficiency. Full article

An aerial Radio Environment Map (REM) characterizes the spatial distribution of Received Signal Strength (RSS) across a geographic space of interest, which is crucial for optimizing wireless communication in the air. Aerial REM construction can rely on Unmanned Aerial Vehicles (UAVs) to autonomously select interesting positions for sampling RSS data, enhancing the quality of construction. However, due to the lack of prior information about the environment, it is challenging for UAVs to determine suitable sampling positions online. Additionally, achieving efficient exploration of the target area through collaboration among multiple UAVs is difficult. To address this issue, this paper proposes a multi-stage approach to data sampling and path planning with multiple UAVs. Specifically, the UAVs’ data sampling task over the target area is divided into multiple stages. By selecting an appropriate stage position, we use the RSS values at that position to determine whether additional data need to be sampled in a specific local area. At each stage, the area is divided into Voronoi diagrams based on the current position of each UAV, assigning each UAV its own region to explore. In our sampling strategy, the probability distribution for sampling is obtained by estimating the RSS and uncertainty of unsampled positions and then taking the weighted sum of these two values. To obtain the shortest flight path for selected sampling positions, we employ a network structure based on self-attention as the policy network, which is trained through the actor–critic framework to obtain an improvement heuristic strategy, replacing traditional manually designed strategies. Experimental results across three different scenarios indicate that the approach improves the quality of aerial REM construction while efficiently planning the shortest paths for UAVs between sampling positions. Full article

In this paper, we explore the connection between sensor networks and graph theory. Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. Specifically, we focus on vertex coloring, Eulerian paths, and Hamiltonian paths within the Delaunay graph associated with a sensor network. These concepts have critical applications in sensor networks, including connectivity analysis, efficient data collection, route optimization, task scheduling, and resource management. We derive theoretical results related to the chromatic number and the existence of Eulerian and Hamiltonian trails in the graph linked to the sensor network. Additionally, we complement this theoretical study with the implementation of several algorithmic procedures. A case study involving the monitoring of a sugarcane field, coupled with a computational analysis, demonstrates the performance and practical applicability of these algorithms in real-world scenarios. Full article

The study of unmanned aerial vehicle (UAV) coverage path planning is of great significance for ensuring maritime situational awareness and monitoring. In response to the problem of maritime multi-region coverage surveillance in complex obstacle environments, this paper proposes a global path planning method capable of simultaneously addressing the multiple traveling salesman problem, coverage path planning problem, and obstacle avoidance problem. Firstly, a multiple traveling salesmen problem–coverage path planning (MTSP-CPP) model with the objective of minimizing the maximum task completion time is constructed. Secondly, a method for calculating obstacle-avoidance path costs based on the Voronoi diagram is proposed, laying the foundation for obtaining the optimal access order. Thirdly, an improved discrete grey wolf optimizer (IDGWO) algorithm integrated with variable neighborhood search (VNS) operations is proposed to perform task assignment for multiple UAVs and achieve workload balancing. Finally, based on dynamic programming, the coverage path points of the area are solved precisely to generate the globally coverage path. Through simulation experiments with scenarios of varying scales, the effectiveness and superiority of the proposed method are validated. The experimental results demonstrate that this method can effectively solve MTSP-CPP in complex obstacle environments. Full article