Network Location Research Articles

A Study on Scan Trajectory Based Effective Localization Method Using Mobile Anchor in Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a collection of different nodes working together in a networked fashion in an ordered way. For many applications, sensor location in wireless sensor networks (WSN) is crucial. It is expensive to equip each sensor node with a GPS receiver. Numerous methods, including range-based and range-free methods, have been put forth in the past to determine the locations of sensor nodes that are randomly distributed. The majority of them make use of unique nodes known as anchor nodes, which are thought to be aware of their own positions. These anchor nodes supply the information that other sensors use to calculate their locations. Massive numbers of sensor nodes are used in wireless sensor networks (WSNs) to gather data about the immediate environment. However, this data is useless until the precise location of the data collection points is known. Sensor node localization in wireless sensor networks (WSNs) is important for a number of applications. The current research study develops a path planning technique to apply mobile anchors to the localization systems that are currently in use. The two mobile anchors supported by the effective trajectories, or effective SCAN, is the focus of the proposed research project. To localise the sensor node and transmit data, this method involves setting the mobile anchors to move in the opposite direction. The advantages of this dual movable anchor are demonstrated by the research study in terms of decreased localization time, localization error, and other factors.

Spatio‐Temporal Trends of the Relationships Between Surface PM2.5 and Its Chemical Constituents Across Three COALESCE Network Locations in India: A Mass Closure Investigation

AbstractCOALESCE, a collaborative network studying the impacts of carbonaceous aerosols, measured PM2.5 mass and its constituents at several regionally representative sites in India. This study reconciles the reconstructed mass (RCM) from PM2.5 chemical constituents with gravimetric PM2.5 mass measured during 2019 at three COALESCE sites (Bhopal, Mesra, and Mysuru). To evaluate the extent of mass closure, the spatiotemporal variability of organic matter/organic carbon (OM/OC), filter sampling artifacts of OC, nitrate, and aerosol liquid water (ALW) were assessed. The annual loss of total particulate OC from Teflon filters varied across Bhopal (0.46–1.35 μg m−3), Mesra (0.30–0.92 μg m−3), and Mysuru (0.16–0.68 μg m−3). Likewise, the range of nitrate volatilization was 0.7–2.7 μg m−3 at Bhopal, 0.5–2 μg m−3 at Mesra, and 1.3–2.2 μg m−3 at Mysuru. Also, the range of ALW mass was 2.6–6.8 μg m−3 at Bhopal, 3.2–9.5 μg m−3 at Mesra, and 0.8–3.8 μg m−3 at Mysuru. Seasonality was observed in the value of OM/OC with an increase during the pre‐monsoon season at all sites viz., Bhopal (2.20), Mesra (1.61), and Mysuru (2.02) compared to their annual averages, suggesting enhanced contribution of secondary organics to OC. The method developed for estimating RCM in this study resulted in better mass closure compared to using conventional RCM algorithms. Overall, this study provides a robust data validation strategy and India‐specific species coefficients for use in the RCM algorithm. The outcomes of this study can guide regional aerosol sampling, chemical analyses, and model–measurement reconciliation efforts.

Identifying and minimizing critical driving range thresholds for electric vehicles in intercity networks

It is well known that range anxiety faced by electric vehicle (EV) drivers nowadays can be effectively mitigated by either increasing the driving range of vehicles or increasing the distribution density of charging stations. To enact an effective research and development plan, vehicle manufacturers should understand well the relationship between the EV adoption and usage rates and the performance level of battery and energy management technologies. On the other hand, the underlying relationship between EV adoption and usage rates and the number and distribution of charging stations is also critical for charging infrastructure investors in determining their construction plans. This paper is dedicated to analytically identifying two critical driving range thresholds and further reducing these driving range thresholds as much as possible by optimally deploying charging station locations in intercity highway networks. These critical thresholds represent the turning points of individual routing convenience conditions or satisfaction levels for long-distance trips, implying that the inconvenient travel restrictions may be largely overcome if the driving range of EVs goes beyond these thresholds. For this purpose, we develop two integer programming models and dynamic programming algorithms for the driving range threshold identification problems, and extend the modeling framework to accommodate two relevant driving range threshold minimization problems as well, which are solved by a branching algorithm encapsulating the aforementioned dynamic programming procedures. The output of this research offers a set of new analytical tools for EV manufacturers to make strategic decisions on extending the driving range of their products and for charging infrastructure investors to make planning decisions on selecting charging station locations.

Topological information embedded convolutional neural network-based lotus effect optimization for path improvisation of the mobile anchors in wireless sensor networks

ABSTRACT Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs’ mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

Analysis of within-pen and between-pen fenceline temporal contact networks in confined feedlot cattle

Though contact networks are important for describing the dynamics for disease transmission and intervention applications, individual animal contact and barriers between animal populations, such as fences, are not often utilized in the construction of these models. The objective of this study was to use contact network analysis to quantify contacts within two confined pens of feedlot cattle and the shared “fenceline” area between the pens at varying temporal resolutions and contact duration to better inform the construction of network-based disease transmission models for cattle within confined-housing systems. Two neighboring pens of feedlot steers were tagged with Real-Time Location System (RTLS) tags. Within-pen contacts were defined with a spatial threshold (SpTh) of 0.71 m and a minimum contact duration (MCD) of either 10 seconds (10 s), 30 seconds (30 s), or 60 seconds (60 s). For the fenceline network location readings were included within an area extending from 1 m on either side of the shared fence. “Fenceline” contacts could only occur between a steer from each pen. Static, undirected, weighted contact networks for within-pen networks and the between-pen network were generated for the full study duration and for daily (24-h), 6-h period, and hourly networks to better assess network heterogeneity. For the full study duration network, the two within-pen networks were densely homogenous. The within-pen networks showed more heterogeneity when smaller timescales (6-h period and hourly) were applied. When contacts were defined with a MCD of 30 s or 60 s, the total number of contacts seen in each network decreased, indicating that most of the contacts observed in our networks may have been transient passing contacts. Cosine similarity was moderate and stable across days for within pen networks. Of the 90 total tagged steers between the two pens, 86 steers (46 steers from Pen 2 and 40 steers from Pen 3) produced at least one contact across the shared fenceline. The total network density for the network created across the shared fenceline between the two pens was 17%, with few contacts at shorter timescales and for MCD of 30 s or 60 s. Overall, the contact networks created here from high-resolution spatial and temporal contact observation data provide estimates for a contact network within commercial US feedlot pens and the contact network created between two neighboring pens of cattle. These networks can be used to better inform pathogen transmission models on social contact networks.

Real-time water quality forecasting in rivers using satellite data and dynamic models: an online system for operational management, control and citizen science

Increasingly scarce water resources and growing global populations have exacerbated the problems of water quality in river systems and freshwaters in general. New monitoring methodologies and tools to democratize access to water quality information are needed if we are to reach ambitious societal objectives such as the UN Sustainable Development Goals and the European Green Deal. Here we present a cloud-based system for producing publicly accessible real time water quality forecasts coupled to novel biosensor technology. Short term forecasts of water quality impairments, e.g., as cyanobacteria blooms, sediment plumes and toxic pollution incidents are increasingly relevant both to citizens and stakeholders. Here, we present a new cloud based system that utilizes satellite data to produce real time forecasts of flow and water quality using a chain of dynamic catchment-scale models at multiple locations in a river network. We demonstrate this new system using two case studies: the River Thames and the Essex Colne River (United Kingdom). These rivers are key water supply sources for London and South-East England, respectively and are of high interest to recreational water users. We show how the new system can predict and forecast water quality, estimate toxicity and connect to citizen science observations using an App (www.aquascope.com) for information synthesis and delivery.

An efficient minimum cost battery charging and discharging scheduling scheme for electric vehicle using hybrid energy valley-pathfinder algorithm

ABSTRACT Emission pollution is one of the major issues in many growing cities. To overcome the problem and to prevent pollution, “Electric Vehicle (EV)” is one of the best solutions. Moreover, it has both economic and environmental benefits. To increase network reliability and quality, distribution companies contribute effective assistance. The major challenge met by this network is choosing an applicable connecting point for the bi-directional operation in a distribution network. Other issues the network faces are the performance and quality of the distribution network, cost reduction, and geographic location. The system’s stability is affected by connecting to the grid for charging EVs. Therefore, the optimal charging process is needed for the EVs. This paper proposes developing an optimal charging system without damaging the stability of the power system. This optimization is carried out by combining two algorithms and developing a new algorithm called “Final Position of Energy Valley and Pathfinder Algorithm (FP-EVPA).” This algorithm minimizes overall system operating costs. Also, it helps to reduce pollution emissions, grid losses, environmental pollution, nodal offset voltage, peak valley difference of load, charging, and operating costs. The optimized method maintains the quality of the power grid and system stability. It is also helpful for the users for minimal charging costs. The empirical findings of the model are better than 0.19%, 0.2%, 0.18%, and 0.07% of GEO, EOO, EVO, and PFA algorithms in terms of median. The developed FP-EVPA was compared with other traditional algorithms with different test functions to examine the effective achievement of the developed method.

Low-voltage characteristic voltage based fault distance estimation method of distribution network

The traditional medium-voltage distribution network fault location method uses mainly the voltage and current measurements of the medium-voltage side, which results in problems such as high installation costs at the measuring points and complicated postoperation and maintenance work. Therefore, a fault location idea based on the distributed measurement of low-voltage side voltage is proposed in this paper. First, the characteristic voltage is adaptively selected according to the fault type. Second, the suspected fault section is determined by comparing the characteristic voltage amplitude of each measuring point. Third, the fault section is located using the section unit characteristic voltage drop defined for each suspected fault section. Finally, fault distance estimation is achieved based on the voltage difference matrix and characteristic voltage analysis. This method achieves accurate fault distance identification based on the distribution difference of the characteristic voltage of the low-voltage side under the fault state. This work provides a new economical and practical idea for determining the fault locations of distribution networks. The effectiveness of this method is evaluated by considering a 10 kV distribution network in Guangdong Province built in PSCAD/EMTDC.

Intelligent Routing in Directional Ad Hoc Networks Through Predictive Directional Heat Map From Spatio-Temporal Deep Learning

By applying a simple shortest/minimum-cost routing algorithm, the mobile ad-hoc network (MANET) with heavy data transmissions may be easily congested if multiple routing paths meet at the same relay node. A path may be unable to provide high throughput since some of its path nodes are helping other paths to forward the heavy traffic. Therefore, those busy nodes should be avoided when a new path is to be established. The task of optimal path seeking becomes more challenging when a MANET is equipped with directional antennas that may cause directional interference with neighboring receivers. The motivation of our research is to build an intelligent proactive routing scheme for MANETs with directional antennas. Our directional routing protocol considers not only the global traffic distribution in different areas of the MANET, but also the properties of directional antennas. It uses spatio-temporal deep learning algorithm to predict the next-time snapshot of directional heat map (DHM), which shows the traffic density distribution in each network location as well as the coverage of each directional antenna. Such a DHM will be used to identify the optimal path that can avoid congested areas as well as the interference from all neighboring directional links. Furthermore, an optimization algorithm is designed to perform optimal path selection. It is able to automatically split a single path into multiple ones that later on get converged again into one path, if the path needs to go around a congested area. Therefore, our routing scheme achieves better quality-of-service (QoS) performance than traditional routing mechanisms such as AODV and OLSR.

Electric vehicle supply equipment location and capacity allocation for fixed-route networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

Earth Fault Location for Non-Directly Grounded Distribution Networks

Earth Fault Location for Non-Directly Grounded Distribution Networks

Predicting failure locations in model end-linked polymer networks

Predicting failure locations in model end-linked polymer networks

A precision functional atlas of personalized network topography and probabilities.

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points.

Why did my seizures start now? Influences of lesion connectivity and genetic etiology on age at seizure onset in focal epilepsy.

Patients with focal, lesional epilepsy present with seizures at variable ages. Larger lesion size and overlap with sensorimotor or default mode network (DMN) have been associated with younger age at seizure onset in cohorts with mixed types of focal cortical dysplasia (FCD). Here, we studied determinants of age at seizure onset in patients with bottom-of-sulcus dysplasia (BOSD), a discrete type of FCD with highly localized epileptogenicity. Eighty-four patients (77% operated) with BOSD were studied. Demographic, histopathologic, and genetic findings were recorded. BOSD volume and anatomical, primary versus association, rostral versus caudal, and functional network locations were determined. Normative functional connectivity analyses were performed using each BOSD as a region of interest in resting-state functional magnetic resonance imaging data of healthy children. Variables were correlated with age at seizure onset. Median age at seizure onset was 5.4 (interquartile range = 2-7.9) years. Of 50 tested patients, 22 had somatic and nine had germline pathogenic mammalian target of rapamycin (mTOR) pathway variants. Younger age at seizure onset was associated with greater BOSD volume (p = .002), presence of a germline pathogenic variant (p = .04), DMN overlap (p = .04), and increased functional connectivity with the DMN (p < .05, false discovery rate corrected). Location within sensorimotor cortex and networks was not associated with younger age at seizure onset in our relatively small but homogenous cohort. Greater lesion size, pathogenic mTOR pathway germline variants, and DMN connectivity are associated with younger age at seizure onset in small FCD. Our findings strengthen the suggested role of DMN connectivity in the onset of FCD-related focal epilepsy and reveal novel contributions of genetic etiology.

A Method for 5G–ICN Seamless Mobility Support Based on Router Buffered Data

The 5G core network adopts a Control and User Plane Separation (CUPS) architecture to meet the challenges of low-latency business requirements. In this architecture, a balance between management costs and User Experience (UE) is achieved by moving User Plane Function (UPF) to the edge of the network. However, cross-UPF handover during communication between the UE and the remote server will cause TCP/IP session interruption and affect continuity of delay-sensitive real-time communication continuity. Information-Centric Networks (ICNs) separate identity and location, and their ability to route based on identity can effectively handle mobility. Therefore, based on the 5G-ICN architecture, we propose a seamless mobility support method based on router buffered data (BDMM), making full use of the ICN’s identity-based routing capabilities to solve the problem of UE cross-UPF handover affecting business continuity. BDMM also uses the ICN router data buffering capabilities to reduce packet loss during handovers. We design a dynamic buffer resource allocation strategy (DBRAS) that can adjust the buffer resource allocation results in time according to network traffic changes and business types to solve the problem of unreasonable buffer resource allocation. Finally, experimental results show that our method outperforms other methods in terms of average packet delay, weighted average packet loss rate, and network overhead. In addition, our method also has good performance in average handover delay.

Research on WSN reliable ranging and positioning algorithm for forest environment

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments.

Photovoltaic hosting capacity assessment of a distributed network based on an improved holomorphic embedding load flow method and stochastic scenario simulation

The increasing proportion of distributed energy in the distribution network poses a significant challenge to effectively absorbing distributed generation (DG). On this premise, the DG hosting capacity (HC) assessment in the future distribution network based on the secure operation boundary of the power grid is necessary. In this paper, an improved holomorphic embedding load flow method (HELM) is employed to construct the relationship between the photovoltaic HC (PVHC) and each constraint to determine the upper limit of the comprehensive PVHC. Furthermore, a screening method of PV locations in the distribution network is constructed to screen the alternative PV locations with strong voltage regulation ability and reduce the alternative multi-point schemes, paving the way for simplifying the PVHC assessment based on a stochastic scenario simulation. Finally, a PVHC assessment based on the stochastic scenario simulation is proposed to expand the applicability of the assessment based on improved HELM. The negative impact of PV uncertainty is quantified by generating stochastic load flow scenarios based on the upper limit of the PVHC from improved HELM. The PVHC assessment in the high-voltage distribution network IEEE30 is analyzed to prove the efficiency and comprehensiveness of PVHC assessment in the distribution network.

Robust optimization of a closed-loop supply chain network based on an improved genetic algorithm in an uncertain environment

Aiming at the network structure design problems of facility location, path planning, and flow distribution in an e-commerce closed-loop supply chain network under an uncertain environment, the uncertainties of both the forward and reverse logistics demands and the impact of facility disruptions are analyzed. A robust optimization model is established using the robust peer-to-peer optimization method. An algorithm is designed that combines the minimum spanning tree algorithm with a two-layer adaptive genetic algorithm (Prim-DMGA) to solve the model. The results show that the Prim-DMGA not only requires less computation time, but also generates solutions of higher quality. In addition, the application of the robust optimization model reduces the adverse effects of uncertain factors on the e-commerce closed-loop supply chain network, thereby improving its robustness.

The impact of ESG performance on firms’ technological innovation: evidence from China

Technological innovation is crucial for creating sustainable corporate value and shaping competitive advantage in the market. ESG, as an indicator of corporate value practices, plays a significant role in enterprise technological innovation. However, there is little empirical evidence to support this claim. This study analyzes the relationship between ESG performance and technological innovation in Chinese A-share listed enterprises from 2011 to 2021. The statistical data shows that strong ESG performance has a significant positive impact on corporate technological innovation. ESG performance can promote corporate technological innovation through external mechanisms, such as enhancing corporate network location and increasing institutional shareholding. Additionally, internal mechanisms, such as reducing labor costs and easing financing constraints, can also promote corporate technological innovation. The impact of ESG performance on corporations exhibits heterogeneity, with ESG performance promoting innovation more strongly among labor-intensive firms, non-state-owned firms, highly competitive industries, and mature firms. Based on the study results, it is recommended that enterprises actively practice ESG development concepts, optimize their equity structure, strengthen information communication with stakeholders, and alleviate problems such as information asymmetry to improve their technological innovation. The government should focus on enterprise characteristics, improve ESG development policies, and promote enterprise innovation through ESG performance.