Spatial Distribution Of Nodes Research Articles

Fluviomorphic trajectories for dryland ephemeral stream channels following extreme flash floods

AbstractEphemeral alluvial streams pose globally significant flood hazards to human habitation in drylands, but sparse data for these regions limit understanding of the character and impacts of extreme flooding. In this study, we document decadal changes in dryland ephemeral channel patterns at two sites in the lower Colorado River Basin (southwestern United States) that were ravaged by extraordinary flash floods in the 1970s: Bronco Creek, Arizona (1971), and Eldorado Canyon, Nevada (1974). We refer to these two floods as ‘fluviomorphic erasure events’, because they produced blank slates for the channels that were gradually moulded by more frequent but much smaller flood events. We studied georectified aerial photos that span ~60 years at each site to show that both study sites recovered to their pre‐flood condition after ~25 years. We employ channel network metrics: stream‐link area (SLA), geometric braiding index and junction‐node density. Each metric decreased during the short‐duration extreme flood erasure events. Subsequently, a fluviomorphic trajectory at a decadal tempo returned the channels to pre‐flood values. The SLA decreased at rates of 3.6%–4.1% per year in the decade following the floods. The extreme flood events decreased the pre‐flood geometric braiding index at the two sites by 56%–68%, and it took 15–24 years for this index to recover to pre‐flood values. In contrast, it took 30–35 years for the channels to recover to a uniform pre‐flood channel form, as indicated by the spatial distribution of bars and junction nodes. Our results document baseline examples of ephemeral stream channel evolution trajectories, as future climatic change will likely accelerate increases in the magnitudes and frequencies of extreme floods and geomorphic erasure events.

Intelligent High-Awareness and Channel-Efficient Adaptive Beaconing Based on Density and Distribution for Vehicular Networks

In vehicle ad hoc networks (VANETs), a beacon is a periodic message sent to nearby vehicles containing essential details like the sender’s vehicle ID, location, speed, and direction. Maintaining the freshness of this information without causing network congestion requires adaptive beaconing to adjust to changes in mobility and network density. Our research, based on extensive simulation experiments, identifies specific parameter sets optimal for adapting beaconing rates to different scenarios. From this analysis, we introduce a novel scheme called high-awareness and channel-efficient adaptive beaconing (HACEAB), employing fuzzy logic to adapt to various environments and conditions. Initially, the protocol gauges network density using an adaptive threshold function, followed by estimating the node spatial distribution through the quadrat statistic method to discern uniform distribution or clustering. Utilizing these data, the protocol adjusts beaconing rates via appropriate input parameters for the fuzzy logic system. Remarkably, HACEAB represents the first beaconing scheme capable of simultaneously adjusting to changes in network density and spatial distribution. Furthermore, the protocol enhances performance by adapting transmission power to fluctuations in node density and distribution. NS-3 simulations validate the efficacy of these improvements.

Optimizing Cervical Target Volume in Patients with Nasopharyngeal Cancer Based On Nodal Drainage Distance.

To determine the potential nodal drainage distances of nasopharyngeal carcinoma (NPC) by investigating spatial distribution of metastatic lymph nodes (LN). Patients with NPC harboring at least two ipsilateral metastatic LNs were enrolled. LN spreading distances were analyzed in nonrestricted direction, cranial-to-caudal direction, and between the two most caudal LNs. Euclidean distance (ED) and vertical distance (VD) between any two LNs were computed. The nearest-neighbor ED and VD covering 95% of LNs or patients (p95-ED and p95-VD) were considered drainage distances, and were further validated by independent internal and external cohorts with recurrent LNs. In all, 5,836 metastatic LNs in 948 patients were contoured. Corresponding to the three scenarios, per-LN level, the p95-EDs were 2.83, 3.28, and 3.55 cm, and p95-VDs were 2.17, 2.32, and 2.63 cm, respectively. Per-patient level, the p95-EDs were 3.25, 3.95, and 3.81 cm, and p95-VDs were 2.67, 2.81, and 2.73 cm, respectively. In internal validation, over 95% of recurred LNs occurred within ED of 2.91 cm and VD of 0.82 cm to the neighbor LN, and the corresponding distances in external validation were 2.77 and 0.67 cm, respectively. In NPC, the maximum LN drainage distance was 3.95 cm without considering the direction. Specifically, in cranial-to-caudal direction, the sufficient vertical drainage distance was 2.81 cm, indicating that a 3-cm extension from the most inferior node may be rational as caudal border of the prophylactic clinical target volume (CTV). These findings promote in-depth understanding of nodal spreading patterns, uncovering paramount evidence for individualized CTV.

An empirical assessment of whether urban green ecological networks have the capacity to store higher levels of carbon

Carbon–neutral growth is a crucial long-term climatic aim in the context of global warming. This paper introduces complex network theory and explores its potential application to achieve this goal. Specifically, we investigate the spatial and temporal distribution of nodes and sources in the ecological network, and examine whether a relationship exists between the topological index of network nodes and the landscape pattern index of ecological source areas. We also determine the contribution of nodes to the carbon stock of the entire network by exploring the correlation between the carbon stock of nodes and sources to develop an optimization strategy based on the synergistic effect of node-source carbon enhancement. Finally, we test the effect of network optimization through robustness. Our results show that: (1) The correlation topological feature index analysis reveals that the degree distribution of the node network's topological characteristics becomes dispersed and modular, exhibiting the characteristics of small-world networks according to a large clustering coefficient. The heterogeneity and extent of ecological source landscapes have increased by modularity index but remain distributed and locally fragmented; (2) According to correlation analysis, by enhancing the eccentricity of the node topology, the patch cohesion index (COHESION) of the ecological source site can maximize the contribution of the node to the enhancement of the carbon stock benefits of the source site; (3) According to the tests on the robustness of nodes and edges and the robustness of network links, network stability is improved and carbon sink capacity is enhanced. Simultaneously, the restoration and rejuvenation of ecological space through national ecological construction projects can effectively improve the carbon sink within the organized region, contributing to the carbon neutrality aim. This research gives scientific and quantifiable references for potential ecological construction projects for sustainable cities and the optimization of urban ecological space structure.

Monte Carlo Simulation of Percolation Phenomena for Direct Current in Large Square Matrices

In this study, an in-depth analysis of the percolation phenomenon for square matrices with dimensions from L = 50 to 600 for a sample number of 5 × 104 was performed using Monte Carlo computer simulations. The percolation threshold value was defined as the number of conductive nodes remaining in the matrix before drawing the node interrupting the last percolation channel, in connection with the overall count of nodes within the matrix. The distributions of percolation threshold values were found to be normal distributions. The dependencies of the expected value (mean) of the percolation threshold and the standard deviation of the dimensions of the matrix were determined. It was established that the standard deviation decreased with the increase in matrix dimensions, ranging from 0.0262253 for a matrix with L = 50 to 0.0044160 for L = 600, which is almost six-fold lower. The mean value of the percolation threshold was practically constant and amounted to approximately 0.5927. The analysis involved not only the spatial distributions of nodes interrupting the percolation channels but also the overall patterns of node interruption in the matrix. The distributions revealed an edge phenomenon within the matrices, characterized by the maximum concentration of nodes interrupting the final percolation channel occurring at the center of the matrix. As they approached the edge of the matrix, their concentration decreased. It was established that increasing the dimensions of the matrix slowed down the rate of decrease in the number of nodes towards the edge. In doing so, the area in which values close to the maximum occurred was expanded. Based on the approximation of the experimental results, formulas were determined describing the spatial distributions of the nodes interrupting the last percolation channel and the values of the standard deviation from the matrix dimensions. The relationships obtained showed that with increasing matrix dimensions, the edge phenomenon should gradually disappear, and the percolation threshold standard deviation values caused by it will tend towards zero.

Adaptive mean center of mass particle swarm optimizer for auto-localization in 3D wireless sensor networks

Wireless Sensor Networks (WSNs) have become instrumental in environmental monitoring, healthcare, agriculture, and industrial automation. In WSNs, the precise localization of sensor nodes is crucial for informed decision-making and network efficiency. This study explores localization in the context of WSNs, focusing on the 6LoWPAN and Zigbee protocols. These protocols are vital for integrating WSNs into the Internet of Things (IoT). We highlight the significance of spatial node distribution and WSNs' challenges, such as resource limitations and signal interference. We emphasize range-based methods due to their accuracy. We propose the Adaptive Mean Center of Mass Particle Swarm Optimizer (AMCMPSO) to address these. Inspired by the center of mass principle, this algorithm adapts parameters for enhanced localization on regular and irregular surfaces. AMCMPSO leverages the principle of the center of mass and mean values to enhance the efficiency of sensor node localization. The algorithm incorporates adaptive parameters, including inertia weight and acceleration coefficients, to improve search efficiency and convergence speed. Our simulations demonstrate the superior performance of AMCMPSO, with an average improvement rate of 99.86%. Moreover, the localization error is consistently below 1.34 cm, ensuring precise spatial awareness. In 3D environments, AMCMPSO consistently delivers coverage rates exceeding 87%, even in challenging scenarios.

On the Impact of Recharging Behavior on Mobility

We consider the behavior of mobile users that, upon depletion of their device's energy, can detour from their regular path, in order to reach a location where the device can be recharged. We develop abstractions of this recharging behavior and analytically derive a charging-aware mobility model. The device is viewed as a mobile node. Based on the Palm inversion formula, we derive the (integral-form) stationary probability density function of the node's location, subject to attraction exerted by one charger in a bounded convex area. The analytical and numerical results demonstrate the distortion effect the charger has on the spatial node distribution. We observe, and explain, a counter-intuitive effect whereby the node density exhibits peaks that are asymmetric, coupled with a relative decrease of the density around the charger area. In addition, we provide a method to approximate the probability density function when multiple chargers are deployed. We examine the accuracy of this approximation and comment on its limitations.

A Generalized Mechano-statistical Transient Network Model for Unravelling the Network Topology and Elasticity of Hydrophobically Associating Multiblock Copolymers in Aqueous Solutions

In this contribution, we unravel the transient network topology and elasticity of micellar networks formed by hydrophobically associating multiblock copolymers in aqueous solutions. Unlike studies on conventional triblock copolymers bearing hydrophobic blocks as end groups, our research focuses on alternating poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO) multiblock copolymers having multiple hydrophobic PPO blocks along the chain. We adopt a combinatorics approach to extend and generalize the mechano-statistical transient network model developed by Annable et al. for telechelic triblock copolymers [ Journal of Rheology, 1993, 37, 695] to multiblock copolymers. The model allows one to predict the concentration dependent elasticity of networks formed by multiblock copolymers with known molecular characteristics by using knowledge of the micellar network microstructure. The spatial distribution of the hydrophobic nodes is inferred from Small-Angle X-ray Scattering (SAXS) by converting the structure factor to the radial distribution function. The number of closely neighboring micellar cores between which an elastic bridge can be formed (nm) is calculated by spherical integration of the radial distribution function up to a distance of the radius of gyration of an intermediate soluble PEO block. Using the evolution of nm with concentration as an input for the model, the predictions show good agreement with experimental elasticity data, as inferred from the plateau modulus in linear shear rheology. The network evolves from loop-dominated, poorly elastic with cross-linking nodes with low functionality at low concentrations to bridge-dominated, highly elastic with higher node functionalities at more elevated concentrations. It is anticipated that our generalized mechano-statistical transient network model can also be used for equally spaced, multisticker associating polymers forming networks by multifunctional interactions other than micellar aggregation.

An Improved Ant Colony Algorithm for Virtual Resource Scheduling in Cloud Computing

In order to solve the problems of uneven spatial distribution of data nodes and unclear weight relationship of virtual scheduling features in cloud computing platform, a virtual resource scheduling method based on improved ant colony algorithm is studied and designed to improve the performance of virtual resource scheduling in cloud computing platform by this method. After analyzing the information resource sequence change of the cloud computing platform, according to the STR - Tree partition graph, a simulated annealing-based algorithm is employed to classify the resource types after optimal scheduling into IO types, middle types and CPU types, and the time span and load balance are set as the measurement indexes. The simulation results show that after applying this method, the occupied resources of the main platform are 535 MB, which are much lower than the other two comparison algorithms, and the method has improved the allocation rationality, resource balance, maximum queue length and energy consumption. This result indicates that applying this virtual resource scheduling method can effectively improve the intelligent scheduling of virtual resources in the cloud computing platform.

Modeling and Analysis of the Local Delay in an MEC-Based VANET for an Urban Area

This article studies the local delay modeling and analysis in a mobile edge computing (MEC) based vehicular ad hoc network (VANET) for an urban scenario where vehicle nodes request for computing services from an edge node. Based on stochastic geometry, theoretical local delay models are derived for uplink transmission of a service request and downlink transmission of a computing result, respectively. In deriving the theoretical models, the road distribution of an urban area is modeled as a Poisson line process (PLP), the spatial distribution of vehicle nodes on each street are modeled as an independent one-dimensional homogeneous Poisson point process (PPP), and edge nodes are deployed along each street with a certain probability. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access, and independent Nakagami-m fading is considered over all wireless links. Based on the derived models, the impacts of major parameters on the local delay are investigated through numerical results. The derived local delay models can be used to help parameter settings in the deployment of edge nodes in an urban area.

Combined Optimization of LID Patches and the Gray Drainage System to Control Wet Weather Discharge Pollution

Low impact development (LID) has emerged as an effective management to control urban runoff. However, for storm drainage systems located in high-density old urban areas with scarce land resources and fragmented landscapes, there are difficulties in the application of LID layouts. To solve the waterlogging and wet weather discharge pollution (WWDP), based on the spatial distribution of available land resources, runoff path, ponding area, overflow nodes, and sediments in drainages, a spatial optimized layout method of LID patches combined with gray drainages was proposed and applied to a typical storm drainage system in Shanghai, China. Only 22.9% of the ground surface and 14.0% of the roof, which accounted for the study area were reconstructed to LIDs, and the optimal LID patches combined with 0.4 m storage capacity depth (SCD) could prevent discharge below 6.5 mm rainfall. The optimal LID-gray drainages increased the reduction ratios of suspended substances (SSs) in WWDP by 37–74% compared with only LID patches in 9.1–21.8 mm rainfall. The “LID patches-gray system” could effectively control WWDP in old urban areas with high frequencies of moderate and light rain. The proposed methodology can be instructive for the sustainable reconstruction of storm drainages inappropriately connected with sewage.

RANGE-BASED NETWORK FORMATION TO SUPPORT GEOREFERENCING OF MAPPING SWARM PLATFORMS

Abstract. Mapping platforms jointly operating in a formation are increasingly used to improve the efficiency of geospatial data acquisition recently. For example, UAS swarm mapping is gaining market share or robot platform are used for indoor mapping. These platforms are typically equipped with imaging and navigation sensors as well as have various communication capabilities. Until now, the platforms are individually navigated and georeferenced. Since the platforms are typically sharing a small area, and thus, they are within their sensing range; for example, they can see and thus track each other in optical or lidar imagery. Furthermore, new communication technologies have started to provide ranging information between communication points. Using the ranges between platforms makes it feasible to create a local geodetic network defined by the platforms. The geometric strength of the network then can be exploited to support platform georeferencing. In this study, the network formation based on ranges is investigated. Initial experiences are reported on the impact of the size of the network, the spatial distribution of the network nodes and the number of available ranges.

Performance Analysis for AOA-Based Localization Under Millimeter-Wave Wireless Networks

Millimeter-Wave (mmWave) communication is a promising solution for achieving high data rate and low latency in 5G wireless networks. Since directional beamforming and antenna arrays are exploited in the mmWave networks, accurate angle-of-arrival (AOA) measurements can be obtained and utilized for localization purposes. In this work, we consider the AOA-based positioning for the mmWave networks using stochastic geometry and analyze how the Cramér-Rao lower bound (CRLB) is affected by the spatial distribution of nodes, including the target and participating anchors. In order to apply the CRLB on a network setting with random node locations, we propose an accurate approximation of the CRLB using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lceil L/4 \rceil $ </tex-math></inline-formula> -th value of the ordered distances where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula> is the number of participating anchors. These findings provide us deep insight into optimum network design that meets specified localization requirements.

Radial Distribution Functions of Entanglements in Primitive Chain Network Simulations

Although the entanglement network among polymers has been widely investigated, the spatial distribution of entanglement nodes has been rarely discussed. In this study, we obtained the radial distribution functions (RDFs) for the entanglement nodes from the snapshots of multi-chain slip-link simulations. The model employed in this study is the primitive chain network (PCN) model, which has been validated to reproduce the entangled polymer dynamics semi-quantitatively. The statistics of captured entanglement networks were compared to those reported for the primitive path network extracted by the CReTA procedure from the full-atomistic molecular model of a polyethylene melt. In the range of distance longer than the average strand length, the network structure from PCN does not show any structural correlation, and the intra-chain correlation is close to that for Gaussian chains with non-interacting slip-links. These features are consistent with the CReTA network and the conventional assumptions made for single-chain models. Meanwhile, in the short-range, the structural correlation in PCN is much weaker than that in CReTA. This discrepancy is because the short-range structure in PCN is realized as a result of imposed fluctuations, whereas it is determined by the minimization of the primitive path in CReTA.

AODV routing protocol performance assessment for wireless sensor network scenarios

Emerging WSN applications include a wide selection of traditional situations with many nodes. The messages are propagated through intermediate nodes, in order to ensure correct contact between the network nodes with the base station, so that a path has many connexions. Generally speaking, the nodes on the sensor network are distinguished by their minimal resources, so protocols must be used that not only guarantee connectivity but also conserve the most energy while maintaining scalability. Different protocols for routing wireless sensor networks have been suggested, however, reactive routing algorithms have been shown to be energy efficient and adaptable to wireless sensor networks. In this paper, under the OMNeT + + simulation platform, the performance of the reactive protocol will be tested, evaluating different scripts. In contrast to the supplementary responsive protocols stated in literature, the AODV protocol has been selected for supremacy in performance. The following parameters have been evaluated: the transmission rate of packets, average delays, overhead routing and energy usage. The findings show that AODV decreases its efficiency with the growth in the number of nodes. As a result, its usability limits the network scalability and the outcomes demonstration that the spatial distribution of nodes affects protocol performance. The standardised delivery shows the best outcomes for our particular works.

A detailed MILP formulation for the optimal design of advanced biofuel supply chains

The optimal design of a biomass supply chain is a complex problem, which must take into account multiple interrelated factors (i.e the spatial distribution of the network nodes, the efficient planning of logistics activities, etc.). Mixed Integer Linear Programming has proven to be an effective mathematical tool for the optimization of the design and the management strategy of Advanced Biofuel Supply Chains (ABSC). This work presents a MILP formulation of the economical optimization of ABSC design, comprising the definition of the associated weekly management plan. A general modeling approach is proposed with a network structure comprising two intermediate echelons (storage and conversion facilities) and accounts for train and truck freight transport. The model is declined for the case of a multi-feedstock ABSC for green methanol production tested on the Italian case study. Residual biomass feedstocks considered are woodchips from primary forestry residues, grape pomace, and exhausted olive pomace. The calculated cost of methanol is equal to 418.7 €/t with conversion facility cost accounting for 50% of the fuel cost share while transportation and storage costs for around 15%. When considering only woodchips the price of methanol increases to 433.4 €/t outlining the advantages of multi-feedstock approach.

Scaling Performance Analysis and Optimization Based on the Node Spatial Distribution in Mobile Content-Centric Networks

Content-centric networks (CCNs) have become a promising technology for relieving the increasing wireless traffic demands. In this paper, we explore the scaling performance of mobile content-centric networks based on the nonuniform spatial distribution of nodes, where each node moves around its own home point and requests the desired content according to a Zipf distribution. We assume each mobile node is equipped with a finite local cache, which is applied to cache contents following a static cache allocation scheme. According to the nonuniform spatial distribution of cache-enabled nodes, we introduce two kinds of clustered models, i.e., the clustered grid model and the clustered random model. In each clustered model, we analyze throughput and delay performance when the number of nodes goes infinity by means of the proposed cell-partition scheduling scheme and the distributed multihop routing scheme. We show that the node mobility degree and the clustering behavior play the fundamental roles in the aforementioned asymptotic performance. Finally, we study the optimal cache allocation problem in the two kinds of clustered models. Our findings provide a guidance for developing the optimal caching scheme. We further perform the numerical simulations to validate the theoretical scaling laws.

Mining and Construction of Information Opportunity Cooperation Mode Based on Big Data Fusion Internet of Things

This paper introduces evolutionary game theory as an analysis tool to study the evolution of the cooperative behavior of nodes in the opportunistic network. At the same time, considering the spatial distribution of nodes in the opportunistic network and the corresponding cooperative interaction relationship, the evolutionary graph theory is further applied to study the cooperative behavior in the context of considering the network space. In the collaboration model of this article, the vertices in the evolutionary graph theory are used to represent the nodes in the network, and the edges in the graph represent that the node pairs are within the communication range of each other. The node will play a game with neighboring nodes according to the payout matrix. Through repeated games between nodes, the network reaches a relatively stable final state, and the distribution of nodes with different behaviors in the final state of the network is checked. By adjusting the network parameters, we can analyze the distribution and evolution of cooperation in the network, and study the stability of the network. We realize the credible collaborative evolutionary game model by building a comprehensive simulation platform and verify its effectiveness and scalability. We design and implement an evolutionary game model independent of routing protocols, so that the model can be quickly deployed in traditional routing protocols. Subsequently, simulations verified that the reputation model played a role in guiding nodes to actively participate in network collaboration during the collaborative evolution process. And the greater the strength of the reputation incentive mechanism, the better the effect of the evolutionary model. The evolution model is deployed in multiple routing protocols, verifying that the model can play a role in different routing protocols. In addition, the evolution model can also play a role in different network environments and has good scalability.

Frequency-dependent functional connectivity in resting state networks.

Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Performance Study for Random Access-Based Wireless Mutual Broadcast Networks With Ginibre Point Processes

While the random access based wireless mutual broadcast (RA-WMB) is expected to be key to various applications including proximal data dissemination and neighbor discovery, such RA-WMB applications may have a wide range of node distributions. Therefore, this letter investigates the impact of spatial node distributions on the RA-WMB performance and design, through modeling node distributions as the β-Ginibre point process (GPP) that expresses the node repulsion via 0 <; β ≤ 1. The results demonstrate that the β-GPP node repulsion may degrade the RA-WMB performance and it rarely affects the RA-WMB operation design for improving the performance.