Position Of Nodes Research Articles

The impact response and failure mechanism of sandwich plates with M-type foldcore under low-velocity impact

Foldcore sandwich structure has promising applications for load-bearing, and in this study, M-type foldcore sandwiches are prepared through a molding and pressing process with fiberglass. To be specific, the sandwich structures are investigated for dynamic response and damage mechanism under low-velocity impacts with various impact positions and energy. The results show that impact position significantly affects the damage mode of the sandwich plate, the damage mode of crush fracture and collapse failure at node position can dissipate higher energy compared with tensile fracture at base position. Moreover, the impact energy shows a certain influence only when the sandwich panel is not penetrated. Besides, numerical prediction closely matches experimental results in terms of load-displacement and energy-displacement histories. Effects of geometric configuration are explored, and the results suggest that although increasing the thickness of panel and core can effectively improve the load-bearing capacity under low energy impacts, increasing the core thickness is a more effective method in lightweight design than increasing the thickness of plane. Furthermore, the impact resistance can be enhanced by selecting the appropriate platform length and narrowing the platform angle. Notably, M-type foldcore sandwich is superior to V-type foldcore sandwich and corrugated sandwich in terms of specific energy absorption.

Seismic Behaviour of CFST Space Intersecting Nodes in an Oblique Mesh

The design of intersecting nodes in high-rise oblique mesh structures is a critical issue. The existing research on the intersecting nodes of oblique meshes mainly focuses on plane intersecting nodes and monotonic axial compression loads. The plane intersecting nodes cannot consider the contribution of the node’s out-of-plane angle and floor beam to the node’s out-of-plane stiffness in actual structures. In this paper, numerical analysis using ABAQUS was conducted to investigate the mechanical performance of space intersecting nodes of oblique meshes (OMSIN) under cyclic axial tension and compression loads, to provide a reference for the engineering application of oblique mesh structures in seismic regions. Six parameters were considered: the space intersecting angle, the plane angle symmetry coefficient, the plane intersecting angle, the out-of-plane constraint restraint, the steel content of the cross-section, and the concrete strength. The study showed that changes in the thickness of the steel tube wall are unfavourable for the uniform transmission of stress. Increasing the space intersecting angle significantly weakened the seismic performance, and the space angle affects the failure mode of the node. Asymmetric arrangements of the upper and lower plane angles caused nonlinear development of out-of-plane. The ultimate load and overall compressive stiffness of the specimen were positively correlated with the plane angle, and vertical constraints should be applied to the node position of components with plane angles greater than or equal to 70°. The out-of-plane constraint was a key factor affecting the seismic performance of the node, and it was proportional to the ultimate load of the component. In structural design, if the aim is to improve the mechanical performance of the component by increasing the steel content, more enormous out-of-plane constraints should be set to control plane external displacement strictly. The concrete strength is proportional to the ultimate axial load and axial stiffness, and its influence on the mechanical performance in the axial tension direction is not significant. Finally, a dimensionless skeleton curve model of the node was established. The existing formula for the bearing capacity of CFST columns was fitted to obtain the calculation formula for the axial yield and ultimate load of the OMSIN under cyclic loads.

PrSLoc: Sybil attack detection for localization with private observers using differential privacy

During the localization process in wireless networks, risks such as Sybil attacks and nodes’ location privacy leakage can exist due to the open and shared nature of wireless networks. However, it is challenging to obtain accurate location for sensor nodes while preserving the privacy of observer nodes who assist in sensor node localization while in the presence of Sybil attacks. Therefore, in this paper, we propose a secure and private localization algorithm, PrSLoc, based on Differential Privacy and Approximate Point-In-Triangulation (APIT, a range-free positioning algorithm). Specifically, APIT is applied for sensor nodes’ localization based on the received signal strength (RSS) from observer nodes as APIT doesn’t require special hardware or additional devices to estimate node positions. Besides, Differential Privacy technique is utilized to protect the privacy of observer nodes’ location information (i.e., nodes’ identity, location, and transmission power). Additionally, we mitigate Sybil attacks using a novel lightweight Sybil detection approach introduced in this paper that is based on the twice difference of the perceived RSS. Simulation results demonstrate that PrSLoc can efficiently eliminate Sybil nodes while preserving the observers’ privacy during wireless localization processes. Furthermore, the computation overhead and storage cost of the proposed PrSLoc are competitive compared with existing work. Our code can be found here: https://github.com/learning-lemon/PrSLoc-Sybil-Attack-Detection-for-Localization-with-Private-Observers-using-Differential-Privacy.git.

NeatSankey: Sankey diagrams with improved readability based on node positioning and edge bundling

Sankey diagrams are widely used to visualize event sequence data. However, when the data volume is large, its readability is affected by dense edge crossings, excessive swing amplitude, and small crossover angles, while it is computationally intensive to obtain an optimal layout. In this paper, we propose NeatSankey, a balanced method that generates Sankey diagrams smoothly. It can be laid out quickly with good readability when Sankey diagrams are very complex. At the same time, to comprehensively evaluate the readability of Sankey diagrams, we use three evaluation metrics: crossing number, swing amplitude, and layout coverage. Firstly, we use a heuristic layout algorithm and a force-directed algorithm to adjust the node layout to minimize the edge crossings and swing amplitude with edge widths considered. Secondly, to better reduce the dense confusion caused by edge crossings, we introduce a edge bundling algorithm based on attribute similarity. We present three evaluations: a comprehensive comparison of our results with state-of-the-art techniques, user studies with thirty volunteers, and a case study of two datasets. Our evaluations demonstrate the effectiveness and practicability of the NeatSankey.

Harris Hawks optimization for ambulance vehicle routing in smart cities

The ambulance routing problem is one of the capacitated ambulance routing problem forms. It deals with injuries and their requests for saving. Therefore, the main aim of the ambulance routing problem is to determine the minimum (i.e., optimum) required distances of between: 1) accident places and the ambulance station; 2) the location of the nearest hospital and the accident places. Although of the efforts proposed in the literature, determining the optimum route is crucial. Therefore, this article seeks to tackle ambulance vehicle routing in smart cities using Harris Hawks Optimization (HHO) algorithm. It attempts to take the victims as quickly as possible and confidently. Several engineering optimization problems confirm that HHO outperforms many well-known Swarm intelligence approaches. In our system, let’s use the node approach to produce a city map. Initially, the control station receives accident site information and sends it to the hospital and the ambulance. The HHO vehicle routing algorithm receives data from the driver; the data includes the location of the accident and the node position of the ambulance vehicle. Then, the driver’s shortest route to the accident scene by the HHO. The locations of the accident and hospital are updated by the driver once the car reaches the accident site. The fastest route (which results in the least travel time) to the hospital is then determined. The HHO can provide offline information for a potential combination of the coordinates of destination and source. Extensive simulation experiments demonstrated that the HHO can provide optimal solutions. Furthermore, performance evaluation experiments demonstrated the superiority of the HHO algorithm over its counterparts (SAODV, TVR, and TBM methods). Furthermore, for ten malicious nodes, the PDF of the algorithm was 0.91, which is higher than the counterparts

Radial basis function based finite element method: Formulation and applications

In the study, a method is proposed and developed for solid analysis that is a hybrid of the radial basis function and the finite element method (RBF-FEM). Based on the finite nodes, the method employs the radial basis functions to produce the shape functions with simplicity, especially when increasing the element node number. The formulation and applications of the method in the analysis of solids are examined. Several numerical examples are carried out to analyze the convergence, accuracy, and computational time cost. Its comeouts are then compared with that by the finite element method. The study also shows the factors that affect the accuracy of the method, such as the radial basis function types, radial basis function shape parameter, node number, and node position in an element. It is shown that the method quickly produces better results with large-node-number elements (seven and eight nodes) in comparison to the conventional numerical method. Discussion on the method and the extensibility of the approach are addressed in the conclusion.

Atmospheric wet deposition in urban and suburban sites across the United States

Compared to rural areas, there is a much poorer understanding of trends in wet atmospheric deposition across urban and suburban areas of the U.S. and how they compare to rural sites over time. The goal of this study was to investigate long-term trends in atmospheric wet deposition of nitrogen (ammonium and nitrate; NH4+ and NO3−), sulfate (SO42−), cations, and chloride (Cl−) for urban and suburban sites in the U.S. using data from the National Atmospheric Deposition Program (NADP) and to determine how long-term deposition trends relate to urbanization. For urban and suburban sites, we found that from the late 1970s to 2020, atmospheric deposition of NO3−, dissolved inorganic N (DIN), SO42−, hydrogen (H+), and magnesium (Mg2+) decreased over time, while deposition of NH4+ and potassium (K+) significantly increased over this period. When sites were categorized based on population density, we found no significant differences in rates of wet deposition between urban and background rural values, but the difference between urban and background rural values increased significantly over time for NO3− and DIN. When sites were categorized based on impervious surface area (ISA), we found that rates of NH4+, DIN, SO42, and calcium (Ca2+) deposition were significantly higher in urban sites (that had greater than 25% ISA) compared to background rural values. There were statistically significant positive relationships between population density and deposition of Mg2+, K+, sodium (Na+), and Cl− and significant positive relationships between % ISA and deposition of NO3− and Mg2+. Together, the results of this study demonstrate the urban enhancement of many forms of atmospheric deposition and the need for increased long-term monitoring of atmospheric deposition in cities and suburban sites throughout the U.S., especially in the western U.S.

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

AbstractCommunication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

Detection and Counter Measure of Packet Misrouting by GPS-Free Positioning of Ad-Hoc Nodes

The ad-hoc networks are a developing area of research with a large and wide variety of applications and related requirements. The nodes participating in an ad-hoc network use different routing protocols to send their packets from one node to another. But most of the time, the ad-hoc network is not suitable for urgent needs. For this, an ad-hoc network GPS-free positioning system can be used in emergency situations to save people in danger. Nodes participating in an ad-hoc network choose the best route from various nodes near them to send information through the complex system. For this, we have used trust dynamic source routing (TDSR) in our research work to determine and design the best route to transmit information, and we designed this system with the aim of being able to be used in emergencies. We have compared our proposed routing protocols with dynamic source routing (DSR) and found out that TDSR is working excellent.

Interlayer Link Prediction in Multiplex Social Networks Based on Multiple Types of Consistency Between Embedding Vectors.

Online users are typically active on multiple social media networks (SMNs), which constitute a multiplex social network. With improvements in cybersecurity awareness, users increasingly choose different usernames and provide different profiles on different SMNs. Thus, it is becoming increasingly challenging to determine whether given accounts on different SMNs belong to the same user; this can be expressed as an interlayer link prediction problem in a multiplex network. To address the challenge of predicting interlayer links, feature or structure information is leveraged. Existing methods that use network embedding techniques to address this problem focus on learning a mapping function to unify all nodes into a common latent representation space for prediction; positional relationships between unmatched nodes and their common matched neighbors (CMNs) are not utilized. Furthermore, the layers are often modeled as unweighted graphs, ignoring the strengths of the relationships between nodes. To address these limitations, we propose a framework based on multiple types of consistency between embedding vectors (MulCEVs). In MulCEV, the traditional embedding-based method is applied to obtain the degree of consistency between the vectors representing the unmatched nodes, and a proposed distance consistency index based on the positions of nodes in each latent space provides additional clues for prediction. By associating these two types of consistency, the effective information in the latent spaces is fully utilized. In addition, MulCEV models the layers as weighted graphs to obtain representation. In this way, the higher the strength of the relationship between nodes, the more similar their embedding vectors in the latent representation space will be. The results of our experiments on several real-world and synthetic datasets demonstrate that the proposed MulCEV framework markedly outperforms current embedding-based methods, especially when the number of training iterations is small.

Dynamic data collection algorithm based on mobile edge computing in underwater internet of things

The Underwater Internet of Things (UIoT) has emerged as one of the prominent technologies in the development of future ocean monitoring systems, where mobile edge elements (such as autonomous underwater vehicles (AUVs)) provide a promising method for the data collection from sensor nodes. However, as an important part of the UIoT, underwater wireless sensor networks (UWSNs) are severely affected by the underwater dynamic environment. For instance, node locations change continuously, which significantly increases the difficulty of data collection. To solve this problem, the concept of an inevitable communication space (ICS) is proposed. The ICS is calculated by analyzing the variation in the position of nodes and the communication range. Furthermore, an ICS-based dynamic data collection algorithm (ICS-DDCA) for UIoT is proposed to collect underwater data. This method utilizes the ICS instead of the initial location of the node for data collection to further improve the performance of the algorithm and shorten the data collection time. The simulation results demonstrate that compared with the energy-efficient data collection over AUV-assisted (EEDA) and data collection algorithms based on probabilistic neighborhood (PNCS-GHA), ICS-DDCA can effectively reduce the collection time, while ensuring the full completion of data collection.

Global structure model modification to improve influential node detection

Improving a network's robustness and information acceleration requires assessing the value of its nodes, which has been a central issue in network research. The concept of centrality is crucial since it allows for determining the most important nodes. It is possible to find prominent nodes with the help of centrality indices, but they have computational complexity and are limited by the singularity function. The global structure model (GSM) is one method that helps find these impactful nodes. One of the problems with using GSM is that it ignores these nodes' local information. To address this issue, we propose that considering the features of each index individually and then combining them can result in more accurate detection of influential nodes. An experiment incorporated four attributes: global and local impacts, random walk structure, and node position. In this research, we simulate a real-world network using the SIRIR model to derive its propagation process and then verify its efficacy with measures like the Jaccard similarity score and Kendall's correlation coefficient. According to the findings of the experiments, the Degree of Centrality of the local features has a substantial effect when combined with GSM.

Improved 3D localization algorithm for large scale wireless sensor networks

As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

A novel Sybil attack detection scheme in mobile IoT based on collaborate edge computing

BackgroundInternet of things (IoT) has been used in many places. IoT make devices connected to the Internet via sensor devices to achieve the interconnection between things and things, people and things. Sybil attacker attacks IoT by imitating the identity of users. Few methods are applicable for mobile IoT in previous Sybil attack detecting methods, while the methods are mainly focus on static IoT.ResultsA distributive and lightweight Sybil attack detection scheme in the mobile IoT is proposed in this paper. This scheme works around received signal strength indications (RSSI). The scheme consists of two rounds. Identity information is sent from member nodes to edge nodes in both of the two rounds. In the first round edge, nodes calculate the possible RSSI interval for each member node; in the second round, they check the RSSI value of member nodes to detect Sybil attacks. Intelligent algorithms are used to predict the position of member nodes, which makes the theoretical interval more accurate. Extensive experimental studies show that in the true and false detection rate, this scheme is superior to many existing schemes.

Enhancing Localization Efficiency and Accuracy in Wireless Sensor Networks

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network.

Interaction networks can be used to predict the functions of unknown proteins using known interactions and proteins with known functions. Many graph theory or diffusion-based methods have been proposed, using the assumption that the topological properties of a protein in a network are related to its biological function. Here we seek to improve function prediction by finding more similar neighbors with a new diffusion-based alignment technique to overcome the topological information loss of the node. In this study, we introduce the Diffusion Alignment Coefficient (DAC) algorithm, which combines diffusion, longest common subsequence, and longest common substring techniques to measure the similarity of two nodes in protein interaction networks. As a proof of concept, our experiments, conducted on a real PPI networks S.cerevisiae and Homo Sapiens, demonstrated that our method obtained better results than competitors for MIPS and MSigDB Collections hallmark gene set functional categories. This is the first study to develop a measure of node function similarity using alignment to consider the positions of nodes in protein-protein interaction networks. According to the experimental results, the use of spatial information belonging to the nodes in the network has a positive effect on the detection of more functionally similar neighboring nodes.

Complex networks analysis: centrality measures

<span lang="EN-US">The centrality of an edge in a graph is proposed to be the degree of sensitivity of a graph distance function to the weight of the edge under consideration. Many centrality metrics are available in network analysis and are effectively used in the investigation of social network properties. Node position is one of them. In this paper, we propose a novel importance of nodes showing how to locate the most essential nodes in a network and to construct a centrality measure for each node in the network, sort the nodes by centralities, and focus on the top ranked nodes, which are the most relevant in terms of this centrality measure. Our research aims to explain how to identify the most important nodes in networks. A centrality metric should be established for each node in the network, and then the nodes based on their centralities, focusing on the top-ranked nodes, which in light of this importance, might be regarded as the most pertinent measure.</span>

Nuclear spin relaxation rate of nonunitary Dirac and Weyl superconductors

Nonunitary superconductivity has attracted renewed interest as a novel gapless phase of matter. In this study, we investigate the superconducting gap structure of nonunitary odd-parity chiral pairing states in a superconductor involving strong spin-orbit interactions. By applying a group theoretical classification of chiral states in terms of discrete rotation symmetry, we categorized all possible point-nodal gap structures in nonunitary chiral states into four types in terms of the topological number of nodes and node positions relative to the rotation axis. In addition to conventional Dirac and Weyl point nodes, we identify a type of Dirac point nodes unique to nonunitary chiral superconducting states. Such a Dirac point node is a nodal point of two pseudospin excitations, but the dispersions around it depend on the pseudospin. The node type can be identified experimentally based on the temperature dependence of the nuclear magnetic resonance longitudinal relaxation rate. The implication of our results for a nonunitary odd-parity superconductor in ${\mathrm{UTe}}_{2}$ is also discussed.

DV-Hop Location Algorithm Based on RSSI Correction

To increase the positioning accuracy of Distance Vector-Hop (DV-Hop) algorithm in non-uniform networks, an improved DV-Hop algorithm based on RSSI correction is proposed. The new algorithm first quantizes hops between two nodes by the ratio of the RSSI value between two nodes and the benchmark RSSI value, divides the hops continuously, calculates the average hop distance according to the Minimum Mean Square Error (MMSE) criterion of the best index based on the quantized hops, and then adds hop distance matching factor to the fitness function of each anchor node into the calculation of the hop distance fitness function to weight the fitness function. The change index value is introduced to obtain more accurate hop distance value, and then the estimation error of unknown node (UN) coordinate is modified by using the distance relationship between the UN and the nearest beacon node (BN), and the modified coordination position is further modified by using the triangle centroid to improve the accuracy of node positioning in the irregular network. The experimental results show that compared with the original DV-Hop, improved DV-Hop1, improved DV-Hop2 and improved DV-Hop3, the localization error of the improved algorithm in this paper is reduced by 58%, 45%, 34%, and 29%, respectively, on average, in the two network environments. Without increasing the hardware cost and energy consumption, the improved algorithm has excellent localization performance.

A Novel Unidirectional Smart Charging Management Algorithm for Electric Buses

The difficulty of controlling the charging of electric buses (EBs) and their effects on network demand are discussed in this study. The solutions suggest a call for worldwide, complex infrastructures that manage EVs and EBs equally. Additionally, the Distribution Network (DN) must be prepared for an increased prevalence of reverse power flow caused by widespread distributed renewable generation. This paper focuses exclusively on EBs since they have higher capacity and predictable charging patterns, which makes them more significant for the DN in the context of a transition to complete vehicle electrification and technologies that are mature enough to be hosted. The proposed algorithm employs the Day-Ahead Energy Market (DAEM) in the Smart Charging (SC) to forecast the network operating circumstances. Additionally, the technique makes it possible to facilitate distributed photovoltaic (PV) generation, allowing network demand to be referenced depending on net demand. It also identifies an appropriate individual charger current per vehicle and per-time-step with load-levelling or peak-shaving as its primary goal. The final real demand demonstrates that a coarse correction of the demand is possible. According to the analysis of the DN voltage profile and associated line losses, the ideal node position location of the CS is dependent on PV penetration.