Learning the parameters of a class of stochastic Lotka-Volterra systems with neural networks

In this paper,we propose a neural network approach to learn the parameters of a class of stochastic Lotka-Volterra systems.Approximations of the mean and covariance matrix of the observational variables are obtained from the Euler-Maruyama discretization of the underlying stochastic differential equations(SDEs),based on which the loss function is built.The stochastic gradient descent method is applied in the neural network training.Numerical experiments demonstrate the effectiveness of our method.

Enhancing Evapotranspiration Estimation: A Bibliometric and Systematic Review of Hybrid Neural Networks in Water Resource Management

Accurate estimation of evapotranspiration(ET)is crucial for efficient water resource management,particularly in the face of climate change and increasing water scarcity.This study performs a bibliometric analysis of 352 articles and a systematic review of 35 peer-reviewed papers,selected according to PRISMA guidelines,to evaluate the performance of Hybrid Artificial Neural Networks(HANNs)in ET estimation.The findings demonstrate that HANNs,particularly those combining Multilayer Perceptrons(MLPs),Recurrent Neural Networks(RNNs),and Convolutional Neural Networks(CNNs),are highly effective in capturing the complex nonlinear relationships and tem-poral dependencies characteristic of hydrological processes.These hybrid models,often integrated with optimization algorithms and fuzzy logic frameworks,significantly improve the predictive accuracy and generalization capabilities of ET estimation.The growing adoption of advanced evaluation metrics,such as Kling-Gupta Efficiency(KGE)and Taylor Diagrams,highlights the increasing demand for more robust performance assessments beyond traditional methods.Despite the promising results,challenges remain,particularly regarding model interpretability,computational efficiency,and data scarcity.Future research should prioritize the integration of interpretability techniques,such as attention mechanisms,Local Interpretable Model-Agnostic Explanations(LIME),and feature importance analysis,to enhance model transparency and foster stakeholder trust.Additionally,improving HANN models’scalability and computational efficiency is crucial,especially for large-scale,real-world applications.Approaches such as transfer learning,parallel processing,and hyperparameter optimization will be essential in overcoming these challenges.This study underscores the transformative potential of HANN models for precise ET estimation,particularly in water-scarce and climate-vulnerable regions.By integrating CNNs for automatic feature extraction and leveraging hybrid architectures,HANNs offer considerable advantages for optimizing water management,particularly agriculture.Addressing challenges related to interpretability and scalability will be vital to ensuring the widespread deployment and operational success of HANNs in global water resource management.

Impact of Land Use Change on Ecosystem Services Values in Danjiangkou Reservoir Area,China in the Context of National Water Network Project Construction

Investigating the ecological impact of land use change in the context of the construction of national water network project is crucial,as it is imperative for achieving the sustainable development goals of the national water network and guaranteeing regional ecological stability.Using the Danjiangkou Reservoir Area(DRA),China as the study area,this paper first examined the spatiotemporal dynamics of natural landscape patterns and ecosystem service values(ESV)in the DRA from 2000 to 2018 and then investigated the spatial clustering characteristics of the ESV using spatial statistical analysis tools.Finally,the patch-generating land use simulation(PLUS)model was used to simulate the natural landscape and future changes in the ESV of the DRA from 2018 to 2028 under four different development scenarios:business as usual(BAU),economic development(ED),ecological protection(EP),and shoreline protection(SP).The results show that:during 2000-2018,the construction of water facilities had a significant impact on regional land use/land cover(LULC)change,with a 24830 ha increase in watershed area.ESV exhibited an increasing trend,with a significant and growing spatial clustering effect.The transformation of farmland to water bodies led to accelerated ESV growth,while the transformation of forest land to farmland led to a decrease in the ESV.Normalized difference vegetation index(NDVI)had the strongest effect on the ESV.ESV exhibited a continuous increase from 2018 to 2028 under all the simulation scenarios.The EP scenario had the greatest increase in ESV,while the ED scenario had the smallest increase.The findings suggest that projected land use patterns under different scenarios have varied impacts on ecosystem services(ESs)and that the management and planning of the DRA should balance social,economic,ecological,and security benefits.nomic,ecological,and security benefits.

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Systematic review and network meta-analysis of different nonsteroidal anti-inflammatory drugs for juvenile idiopathic arthritis

BACKGROUND Various non-steroidal anti-inflammatory drugs(NSAIDs)have been used for juvenile idiopathic arthritis(JIA).However,the optimal method for JIA has not yet been developed.AIM To perform a systematic review and network meta-analysis to determine the optimal instructions.METHODS We searched for randomized controlled trials(RCTs)from PubMed,EMBASE,Google Scholar,CNKI,and Wanfang without restriction for publication date or language at August,2023.Any RCTs that comparing the effectiveness of NSAIDs with each other or placebo for JIA were included in this network meta-analysis.The surface under the cumulative ranking curve(SUCRA)analysis was used to rank the treatments.P value less than 0.05 was identified as statistically significant.RESULTS We included 8 RCTs(1127 patients)comparing 8 different instructions including meloxicam(0.125 qd and 0.250 qd),Celecoxib(3 mg/kg bid and 6 mg/kg bid),piroxicam,Naproxen(5.0 mg/kg/d,7.5 mg/kg/d and 12.5 mg/kg/d),inuprofen(30-40 mg/kg/d),Aspirin(60-80 mg/kg/d,75 mg/kg/d,and 55 mg/kg/d),Tolmetin(15 mg/kg/d),Rofecoxib,and placebo.There were no significant differences between any two NSAIDs regarding ACR Pedi 30 response.The SUCRA shows that celecoxib(6 mg/kg bid)ranked first(SUCRA,88.9%),rofecoxib ranked second(SUCRA,68.1%),Celecoxib(3 mg/kg bid)ranked third(SUCRA,51.0%).There were no significant differences between any two NSAIDs regarding adverse events.The SUCRA shows that placebo ranked first(SUCRA,88.2%),piroxicam ranked second(SUCRA,60.5%),rofecoxib(0.6 mg/kg qd)ranked third(SUCRA,56.1%),meloxicam(0.125 mg/kg qd)ranked fourth(SUCRA,56.1%),and rofecoxib(0.3 mg/kg qd)ranked fifth(SUCRA,56.1%).CONCLUSION In summary,celecoxib(6 mg/kg bid)was found to be the most effective NSAID for treating JIA.Rofecoxib,piroxicam,and meloxicam may be safer options,but further research is needed to confirm these findings in larger trials with higher quality studies.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system(ARDS),and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor(PMSM)and the open-circuit faults of Voltage Source Inverter(VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them.Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network(Mr Net) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that Mr Net has the capability of diagnosing the fault types accurately under the interference of noise signals(Laplace noise and Gaussian noise).

The optimal atropine concentration for myopia control in Chinese children: a systematic review and network Metaanalysis

AIM:To figure out whether various atropine dosages may slow the progression of myopia in Chinese kids and teenagers and to determine the optimal atropine concentration for effectively slowing the progression of myopia.METHODS:A systematic search was conducted across the Cochrane Library,PubMed,Web of Science,EMBASE,CNKI,CBM,VIP,and Wanfang database,encompassing literature on slowing progression of myopia with varying atropine concentrations from database inception to January 17,2024.Data extraction and quality assessment were performed,and a network Meta-analysis was executed using Stata version 14.0 Software.Results were visually represented through graphs.RESULTS:Fourteen papers comprising 2475 cases were included;five different concentrations of atropine solution were used.The network Meta-analysis,along with the surface under the cumulative ranking curve(SUCRA),showed that 1%atropine(100%)>0.05%atropine(74.9%)>0.025%atropine(51.6%)>0.02%atropine(47.9%)>0.01%atropine(25.6%)>control in refraction change and 1%atropine(98.7%)>0.05%atropine(70.4%)>0.02%atropine(61.4%)>0.025%atropine(42%)>0.01%atropine(27.4%)>control in axial length(AL)change.CONCLUSION:In Chinese children and teenagers,the five various concentrations of atropine can reduce the progression of myopia.Although the network Meta-analysis showed that 1%atropine is the best one for controlling refraction and AL change,there is a high incidence of adverse effects with the use of 1%atropine.Therefore,we suggest that 0.05%atropine is optimal for Chinese children to slow myopia progression.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Research on a TOPSIS energy efficiency evaluation system for crude oil gathering and transportation systems based on a GA-BP neural network

As the main link of ground engineering,crude oil gathering and transportation systems require huge energy consumption and complex structures.It is necessary to establish an energy efficiency evaluation system for crude oil gathering and transportation systems and identify the energy efficiency gaps.In this paper,the energy efficiency evaluation system of the crude oil gathering and transportation system in an oilfield in western China is established.Combined with the big data analysis method,the GA-BP neural network is used to establish the energy efficiency index prediction model for crude oil gathering and transportation systems.The comprehensive energy consumption,gas consumption,power consumption,energy utilization rate,heat utilization rate,and power utilization rate of crude oil gathering and transportation systems are predicted.Considering the efficiency and unit consumption index of the crude oil gathering and transportation system,the energy efficiency evaluation system of the crude oil gathering and transportation system is established based on a game theory combined weighting method and TOPSIS evaluation method,and the subjective weight is determined by the triangular fuzzy analytic hierarchy process.The entropy weight method determines the objective weight,and the combined weight of game theory combines subjectivity with objectivity to comprehensively evaluate the comprehensive energy efficiency of crude oil gathering and transportation systems and their subsystems.Finally,the weak links in energy utilization are identified,and energy conservation and consumption reduction are improved.The above research provides technical support for the green,efficient and intelligent development of crude oil gathering and transportation systems.

Intrusion Detection System for Smart Industrial Environments with Ensemble Feature Selection and Deep Convolutional Neural Networks

Smart Industrial environments use the Industrial Internet of Things(IIoT)for their routine operations and transform their industrial operations with intelligent and driven approaches.However,IIoT devices are vulnerable to cyber threats and exploits due to their connectivity with the internet.Traditional signature-based IDS are effective in detecting known attacks,but they are unable to detect unknown emerging attacks.Therefore,there is the need for an IDS which can learn from data and detect new threats.Ensemble Machine Learning(ML)and individual Deep Learning(DL)based IDS have been developed,and these individual models achieved low accuracy;however,their performance can be improved with the ensemble stacking technique.In this paper,we have proposed a Deep Stacked Neural Network(DSNN)based IDS,which consists of two stacked Convolutional Neural Network(CNN)models as base learners and Extreme Gradient Boosting(XGB)as the meta learner.The proposed DSNN model was trained and evaluated with the next-generation dataset,TON_IoT.Several pre-processing techniques were applied to prepare a dataset for the model,including ensemble feature selection and the SMOTE technique.Accuracy,precision,recall,F1-score,and false positive rates were used to evaluate the performance of the proposed ensemble model.Our experimental results showed that the accuracy for binary classification is 99.61%,which is better than in the baseline individual DL and ML models.In addition,the model proposed for IDS has been compared with similar models.The proposed DSNN achieved better performance metrics than the other models.The proposed DSNN model will be used to develop enhanced IDS for threat mitigation in smart industrial environments.

Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

A Review of Generative Adversarial Networks for Intrusion Detection Systems: Advances, Challenges, and Future Directions

The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems(IDSs).IDSs have become a research hotspot and have seen remarkable performance improvements.Generative adversarial networks(GANs)have also garnered increasing research interest recently due to their remarkable ability to generate data.This paper investigates the application of(GANs)in(IDS)and explores their current use within this research field.We delve into the adoption of GANs within signature-based,anomaly-based,and hybrid IDSs,focusing on their objectives,methodologies,and advantages.Overall,GANs have been widely employed,mainly focused on solving the class imbalance issue by generating realistic attack samples.While GANs have shown significant potential in addressing the class imbalance issue,there are still open opportunities and challenges to be addressed.Little attention has been paid to their applicability in distributed and decentralized domains,such as IoT networks.Efficiency and scalability have been mostly overlooked,and thus,future works must aim at addressing these gaps.

Research on the Evolution of Country-Level BGP Network Ecosystems in 24 Years

The global Internet is composed of more than 70,000 autonomous domain networks interconnected through the Border Gateway Protocol(BGP).Studying the ecological evolution of BGP network is of great significance for analyzing the evolution trend of the global Internet.This paper focuses on the evolution of Country-Level BGP network ecosystems in 24 years,and innovatively studies the relationship between Country-Level BGP network and economy,breaking through the limitations of traditional research that only focuses on BGP network.The results revealed that the number of global BGP networks has increased by nearly 23 times and that network interconnection has increased nearly 80 times over in 24 years.It was found that the growth of the global BGP network ecosystem has slowed overall due to major global security events,although the BGP network ecosystem in some Southeast Asian countries is developing against the trend.At the same time,there is a significant positive correlation between the BGP network ecology and the national economy in the time dimension;there is a strong positive correlation in the spatial dimension,but the trend is weakening year by year.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Resilience Augmentation in Unmanned Weapon Systems via Multi-Layer Attention Graph Convolutional Neural Networks

The collective Unmanned Weapon System-of-Systems(UWSOS)network represents a fundamental element in modern warfare,characterized by a diverse array of unmanned combat platforms interconnected through hetero-geneous network architectures.Despite its strategic importance,the UWSOS network is highly susceptible to hostile infiltrations,which significantly impede its battlefield recovery capabilities.Existing methods to enhance network resilience predominantly focus on basic graph relationships,neglecting the crucial higher-order dependencies among nodes necessary for capturing multi-hop meta-paths within the UWSOS.To address these limitations,we propose the Enhanced-Resilience Multi-Layer Attention Graph Convolutional Network(E-MAGCN),designed to augment the adaptability of UWSOS.Our approach employs BERT for extracting semantic insights from nodes and edges,thereby refining feature representations by leveraging various node and edge categories.Additionally,E-MAGCN integrates a regularization-based multi-layer attention mechanism and a semantic node fusion algo-rithm within the Graph Convolutional Network(GCN)framework.Through extensive simulation experiments,our model demonstrates an enhancement in resilience performance ranging from 1.2% to 7% over existing algorithms.

Prediction-Based State Estimation and Compensation Control for Networked Systems With Communication Constraints and DoS Attacks

Dear Editor,This letter investigates the output tracking control issue of networked control systems(NCSs)with communication constraints and denial-of-service(DoS)attacks in the sensor-to-controller channel,both of which would induce random network delays.

Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful activity.The DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS research.The model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation accuracy.Thus,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in validation.Furthermore,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 scores.The macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are 0.942.Furthermore,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies precisely.The research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network security.The study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this field.Upcoming research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

Wireless Networked Cognitive Radar System:Overview and Design Guidelines

Cognitive radar is a concept proposed by Simon Haykin in 2006 as a new generation of radar system that imitates human cognitive features.Different from the adaptive signal processing at the receiver in adaptive radar,the cognitive radar realizes closedloop adaptive policy adjustment of both transmitter and receiver in the continuous interaction with the environment.As a networked radar may significantly enhance the flexibility and robustness than its monostatic counterpart,the wireless networked cognitive radar(WNCR)attracts increasing research.This article firstly reviews the concept and development of cognitive radar,especially the related researches of networked cognitive radar.Then,the co-design of cognitive radar and communication is investigated.Although the communication quality between radar sensing nodes is the premise of detection,tracking,imaging and anti-jamming performance of the WNCR,the latest researches seldom consider the communication architecture design for WNCR.Therefore,this article mainly focuses on the proposal of WNCR concept based on the researches of cognitive radar and analyzes research challenges of WNCR system in practical application,and the corresponding guidelines are proposed to inspire future research.