The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nod...The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.展开更多
Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calcula...Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.展开更多
In the bearings-only target tracking, wireless sensor network （WSN） collects observations of the target direction at various nodes and uses an adaptive filter to combine them for target tracking. An efficient networ...In the bearings-only target tracking, wireless sensor network （WSN） collects observations of the target direction at various nodes and uses an adaptive filter to combine them for target tracking. An efficient network management is necessary to gain an optimal tradeoffbetween locating accuracy and energy consumption. This article proposes a self-organizing target tracking algorithm to select the most beneficial subset of nodes to track the target at every snapshot. Compared with traditional methods, this scheme avoids the need for keeping global position information of the network as in greedy selection. Each node judges its future usefulness depending on the knowledge of its own position and using simple mathematics computation. Simulations indicate that this scheme has locating accuracy comparable to the global greedy algorithm. Also, it has good robustness against node failure and autonomous adaptability to the change of the network scale. Furthermore, this algorithm consumes limited energy because only a portion of nodes partakes in the selection at every snapshot.展开更多
To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SO...To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.展开更多
In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),dee...In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.展开更多
It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sens...It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.展开更多
Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,c...Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,cognitive radio networks(CRNs)may also impose some challenges due to the ever increasing complexity of network architecture,the increasing complexity with configuration and management of large-scale networks,fluctuating nature of the available spectrum,diverse Quality-of-Service(QoS)requirements of various applications,and the intensifying difficulties of centralized control,etc.Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm.In this paper,fundamentals of CR,including spectrum sensing,spectrum management,spectrum mobility and spectrum sharing,have been surveyed,with their paradigms of self-organization being emphasized.Variant aspects of selforganization paradigms in CRNs,including critical functionalities of Media Access Control(MAC)- and network-layer operations,are surveyed and compared.Furthermore,new directions and open problems in CRNs are also identified in this survey.展开更多
In networked control systems （NCS）,the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic ...In networked control systems （NCS）,the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol stack.In this paper,a novel architecture for the reconfigurable protocol stack is proposed,which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication standards.Within the architecture,a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol stack.Especially,the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode,routing and scheduling table.Finally,the study on the protocol of dynamic address management is conducted for the system of controller area network （CAN）.The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.展开更多
Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compres...Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.展开更多
Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been...Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions.展开更多
Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commo...Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD...This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD)approach based on multi-agent deep reinforcement learning(MADRL).In high-dynamic and multi-target aviation communication environments,the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information.This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning(DRL)approaches.In response,we design a distributed multi-agent decision architecture(DMADA).We formulate multi-jammer resource allocation as a multiagent Markov decision process(MDP)and propose a fingerprint-based double deep Q-Network(FBDDQN)algorithm for solving it.Each jammer functions as an agent that interacts with the environment in this framework.Through the design of a reasonable reward and training mechanism,our approach enables jammers to achieve distributed cooperation,significantly improving the jamming success rate while considering jamming power cost,and reducing the transmission rate of links.Our experimental results show the FBDDQN algorithm is superior to the baseline methods.展开更多
Wireless Sensor Networks(WSN)have gained significant attention over recent years due to their extensive applications in various domains such as environmentalmonitoring,healthcare systems,industrial automation,and smar...Wireless Sensor Networks(WSN)have gained significant attention over recent years due to their extensive applications in various domains such as environmentalmonitoring,healthcare systems,industrial automation,and smart cities.However,such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities.Thepaper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks.Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user errors or malicious activities.A proposed integrated approach for data collection,preprocessing,and analysis in WSN outlines a series of steps applicable throughout both the design phase and operation stage.This ensures effective detection of attacks and anomalies within WSNs.An introduced attackmodel specifies potential types of unauthorized network layer attacks targeting network nodes,transmitted data,and services offered by the WSN.Furthermore,a graph-based analytical framework was designed to detect attacks by evaluating real-time events from network nodes and determining if an attack is underway.Additionally,a simulation model based on sequences of imperative rules defining behaviors of both regular and compromised nodes is presented.Overall,this technique was experimentally verified using a segment of a WSN embedded in a smart city infrastructure,simulating a wormhole attack.Results demonstrate the viability and practical significance of the technique for enhancing future information security measures.Validation tests confirmed high levels of accuracy and efficiency when applied specifically to detecting wormhole attacks targeting routing protocols in WSNs.Precision and recall rates averaged above the benchmark value of 0.95,thus validating the broad applicability of the proposed models across varied scenarios.展开更多
Network virtualization is the development trend and inevitable requirement of hybrid wireless sensor networks(HWSNs).Low mapping efficiency and service interruption caused by mobility seriously affect the reliability ...Network virtualization is the development trend and inevitable requirement of hybrid wireless sensor networks(HWSNs).Low mapping efficiency and service interruption caused by mobility seriously affect the reliability of sensing tasks and ultimately affect the long-term revenue of the infrastructure providers.In response to these problems,this paper proposes an efficient virtual network embedding algorithm with a reliable service guarantee.Based on the topological attributes of nodes,a method for evaluating the physical network resource importance degree is proposed,and the nodes with rich resources are selected to improve embedding efficiency.Then,a method for evaluating the physical network reliability degree is proposed to predict the probability of mobile sensors providing uninterrupted services.The simulation results show that the proposed algorithm improves the acceptance rate of virtual sensor networks(VSN)embedding requests and the long-term revenue of the infrastructure providers.展开更多
A Wireless Sensor Network(WSN)comprises a series of spatially distributed autonomous devices,each equipped with sophisticated sensors.These sensors play a crucial role in monitoring diverse environmental conditions su...A Wireless Sensor Network(WSN)comprises a series of spatially distributed autonomous devices,each equipped with sophisticated sensors.These sensors play a crucial role in monitoring diverse environmental conditions such as light intensity,air pressure,temperature,humidity,wind,etc.These sensors are generally deployed in harsh and hostile conditions;hence they suffer from different kinds of faults.However,identifying faults in WSN data remains a complex task,as existing fault detection methods,including centralized,distributed,and hybrid approaches,rely on the spatio⁃temporal correlation among sensor nodes.Moreover,existing techniques predominantly leverage classification⁃based machine learning methods to discern the fault state within WSN.In this paper,we propose a regression⁃based bagging method to detect the faults in the network.The proposed bagging method is consisted of GRU(Gated Recurrent Unit)and Prophet model.Bagging allows weak learners to combine efforts to outperform a strong learner,hence it is appropriate to use in WSN.The proposed bagging method was first trained at the base station,then they were deployed at each SN(Sensor Node).Most of the common faults in WSN,such as transient,intermittent and permanent faults,were considered.The validity of the proposed scheme was tested using a trusted online published dataset.Using experimental studies,compared to the latest state⁃of⁃the⁃art machine learning models,the effectiveness of the proposed model is shown for fault detection.Performance evaluation in terms of false positive rate,accuracy,and false alarm rate shows the efficiency of the proposed algorithm.展开更多
Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures a...Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time.To address this issue,this paper presents an innovative energy-efficient protocol based on deep Q-learning(DQN),specifically developed to prolong the operational lifespan of WSNs used in border surveillance.By harnessing the adaptive power of DQN,the proposed protocol dynamically adjusts node activity and communication patterns.This approach ensures optimal energy usage while maintaining high coverage,connectivity,and data accuracy.The proposed system is modeled with 100 sensor nodes deployed over a 1000 m×1000 m area,featuring a strategically positioned sink node.Our method outperforms traditional approaches,achieving significant enhancements in network lifetime and energy utilization.Through extensive simulations,it is observed that the network lifetime increases by 9.75%,throughput increases by 8.85%and average delay decreases by 9.45%in comparison to the similar recent protocols.It demonstrates the robustness and efficiency of our protocol in real-world scenarios,highlighting its potential to revolutionize border surveillance operations.展开更多
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ...Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.展开更多
This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different...This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments.The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency.A novel closed-form expression is also derived for a generalized asymptotic error variance steady state.Steady and convergence analyses are then presented for the synchronization,with frequency adaptations done using least mean square(LMS),the Newton search,the gradient descent(GraDes),the normalized LMS(N-LMS),and the Sign-Data LMS algorithms.Results obtained from real-time experiments showed a better performance of our protocols as compared to the Average Proportional-Integral Synchronization Protocol(AvgPISync)regarding the impact of quantization error on synchronization accuracy,precision,and convergence time.This generalized approach to time synchronization allows flexibility in selecting a suitable protocol for different wireless sensor network applications.展开更多
Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a n...Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions,leveraging a piecewise function to achieve a broad chaotic range()and a high Lyapunov exponent(5.04).Validated through nine benchmarks,including standard randomness tests,Diehard tests,and Shannon entropy(3.883),SPCM demonstrates superior randomness and high sensitivity to initial conditions.Applied to image encryption,SPCM achieves 0.152582 s(39%faster than some techniques)and 433.42 KB/s throughput(134%higher than some techniques),setting new benchmarks for chaotic map-based methods in WSNs.Chaos-based permutation and exclusive or(XOR)diffusion yield near-zero correlation in encrypted images,ensuring strong resistance to Statistical Attacks(SA)and accurate recovery.SPCM also exhibits a strong avalanche effect(bit difference),making it an efficient,secure solution for WSNs in domains like healthcare and smart cities.展开更多
基金supported by the National Natural Science Foundation of China(No.62401597)the Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Scientific Research Project of National University of Defense Technology,China(No.ZK22-02)。
文摘The information exchange among satellites is crucial for the implementation of cluster satellite cooperative missions.However,achieving fast perception,rapid networking,and highprecision time synchronization among nodes without the support of the Global Navigation Satellite System(GNSS)and other prior information remains a formidable challenge to real-time wireless networks design.Therefore,a self-organizing network methodology based on multi-agent negotiation is proposed,which autonomously determines the master node through collaborative negotiation and competitive elections.On this basis,a real-time network protocol design is carried out and a high-precision time synchronization method with motion compensation is proposed.Simulation results demonstrate that the proposed method enables rapid networking with the capability of selfdiscovery,self-organization,and self-healing.For a cluster of 8 satellites,the networking time and the reorganization time are less than 4 s.The time synchronization accuracy exceeds 10-10s with motion compensation,demonstrating excellent real-time performance and stability.The research presented in this paper provides a valuable reference for the design and application of spacebased self-organizing networks for satellite cluster.
基金Supported by the Key Projection of Science and Technology Research of Ministry of Education of China (107057)the Science & Technology Fund for Students of Hohai University (K200803)
文摘Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.
基金National Natural Science Foundation of China （60532030） National Science Fund for Distinguished Young Scholars （60625102） Ph.D. Programs Foundation of Ministry of Education of China （200802240）
文摘In the bearings-only target tracking, wireless sensor network （WSN） collects observations of the target direction at various nodes and uses an adaptive filter to combine them for target tracking. An efficient network management is necessary to gain an optimal tradeoffbetween locating accuracy and energy consumption. This article proposes a self-organizing target tracking algorithm to select the most beneficial subset of nodes to track the target at every snapshot. Compared with traditional methods, this scheme avoids the need for keeping global position information of the network as in greedy selection. Each node judges its future usefulness depending on the knowledge of its own position and using simple mathematics computation. Simulations indicate that this scheme has locating accuracy comparable to the global greedy algorithm. Also, it has good robustness against node failure and autonomous adaptability to the change of the network scale. Furthermore, this algorithm consumes limited energy because only a portion of nodes partakes in the selection at every snapshot.
基金supported by the 863 Program (2015AA01A705)NSFC (61271187)
文摘To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.
文摘In the context of the rapid iteration of information technology,the Internet of Things(IoT)has established itself as a pivotal hub connecting the digital world and the physical world.Wireless Sensor Networks(WSNs),deeply embedded in the perception layer architecture of the IoT,play a crucial role as“tactile nerve endings.”A vast number of micro sensor nodes are widely distributed in monitoring areas according to preset deployment strategies,continuously and accurately perceiving and collecting real-time data on environmental parameters such as temperature,humidity,light intensity,air pressure,and pollutant concentration.These data are transmitted to the IoT cloud platform through stable and reliable communication links,forming a massive and detailed basic data resource pool.By using cutting-edge big data processing algorithms,machine learning models,and artificial intelligence analysis tools,in-depth mining and intelligent analysis of these multi-source heterogeneous data are conducted to generate high-value-added decision-making bases.This precisely empowers multiple fields,including agriculture,medical and health care,smart home,environmental science,and industrial manufacturing,driving intelligent transformation and catalyzing society to move towards a new stage of high-quality development.This paper comprehensively analyzes the technical cores of the IoT and WSNs,systematically sorts out the advanced key technologies of WSNs and the evolution of their strategic significance in the IoT system,deeply explores the innovative application scenarios and practical effects of the two in specific vertical fields,and looks forward to the technological evolution trends.It provides a detailed and highly practical guiding reference for researchers,technical engineers,and industrial decision-makers.
文摘It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.
基金ACKNOWLEDGEMENT This work was supported by National Natural Science Foundation of China （No. 61172050）, Program for New Century Excellent Talents in University （NECT-12-0774）, the open research fund of National Mobile Communications Research Laboratory, Southeast University （No.2013D12）, the Foundation of Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services. The corresponding author is Dr. Zhongshan Zhang.
文摘Cognitive radio(CR) is regarded as a promising technology for providing a high spectral efficiency to mobile users by using heterogeneous wireless network architectures and dynamic spectrum access techniques.However,cognitive radio networks(CRNs)may also impose some challenges due to the ever increasing complexity of network architecture,the increasing complexity with configuration and management of large-scale networks,fluctuating nature of the available spectrum,diverse Quality-of-Service(QoS)requirements of various applications,and the intensifying difficulties of centralized control,etc.Spectrum management functions with self-organization features can be used to address these challenges and realize this new network paradigm.In this paper,fundamentals of CR,including spectrum sensing,spectrum management,spectrum mobility and spectrum sharing,have been surveyed,with their paradigms of self-organization being emphasized.Variant aspects of selforganization paradigms in CRNs,including critical functionalities of Media Access Control(MAC)- and network-layer operations,are surveyed and compared.Furthermore,new directions and open problems in CRNs are also identified in this survey.
基金supported by National Natural Science Foundation of China (No. 60674081,No. 60834002,No. 61074145)
文摘In networked control systems （NCS）,the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol stack.In this paper,a novel architecture for the reconfigurable protocol stack is proposed,which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication standards.Within the architecture,a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol stack.Especially,the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode,routing and scheduling table.Finally,the study on the protocol of dynamic address management is conducted for the system of controller area network （CAN）.The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.
文摘Image compression consists of two main parts: encoding and decoding. One of the important problems of the fractal theory is the long encoding implementation time, which hindered the acceptance of fractal image compression as a practical method. The long encoding time results from the need to perform a large number of domain-range matches, the total encoding time is the product of the number of matches and the time to perform each match. In order to improve encoding speed, a hybrid method combining features extraction and self-organization network has been provided, which is based on the feature extraction approach the comparison pixels by pixels between the feature of range blocks and domains blocks. The efficiency of the new method was been proved by examples.
文摘Wireless networks support numerous terminals,manage large data volumes,and provide diverse services,but the vulnerability to environmental changes leads to increased complexity and costs.Situational awareness has been widely applied in network management,but existing methods fail to find optimal solutions due to the high heterogeneity of base stations,numerous metrics,and complex intercell dependencies.To address this gap,this paper proposes a specialized framework for wireless networks,integrating an evaluation model and control approach.The framework expands the indicator set into four key areas,introduces an evaluation method,and proposes the indicator perturbation greedy(IPG)algorithm and the adjustment scheme selection method based on damping coefficient(DCSS)for effective network optimization.A case study in an urban area demonstrates the framework’s ability to balance and improve network performance,enhancing situational awareness and operational efficiency under dynamic conditions.
文摘Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
基金supported in part by the National Natural Science Foundation of China(No.61906156).
文摘This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD)approach based on multi-agent deep reinforcement learning(MADRL).In high-dynamic and multi-target aviation communication environments,the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information.This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning(DRL)approaches.In response,we design a distributed multi-agent decision architecture(DMADA).We formulate multi-jammer resource allocation as a multiagent Markov decision process(MDP)and propose a fingerprint-based double deep Q-Network(FBDDQN)algorithm for solving it.Each jammer functions as an agent that interacts with the environment in this framework.Through the design of a reasonable reward and training mechanism,our approach enables jammers to achieve distributed cooperation,significantly improving the jamming success rate while considering jamming power cost,and reducing the transmission rate of links.Our experimental results show the FBDDQN algorithm is superior to the baseline methods.
基金the International Scientific Complex“Astana”was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP19680345).
文摘Wireless Sensor Networks(WSN)have gained significant attention over recent years due to their extensive applications in various domains such as environmentalmonitoring,healthcare systems,industrial automation,and smart cities.However,such networks are inherently vulnerable to different types of attacks because they operate in open environments with limited resources and constrained communication capabilities.Thepaper addresses challenges related to modeling and analysis of wireless sensor networks and their susceptibility to attacks.Its objective is to create versatile modeling tools capable of detecting attacks against network devices and identifying anomalies caused either by legitimate user errors or malicious activities.A proposed integrated approach for data collection,preprocessing,and analysis in WSN outlines a series of steps applicable throughout both the design phase and operation stage.This ensures effective detection of attacks and anomalies within WSNs.An introduced attackmodel specifies potential types of unauthorized network layer attacks targeting network nodes,transmitted data,and services offered by the WSN.Furthermore,a graph-based analytical framework was designed to detect attacks by evaluating real-time events from network nodes and determining if an attack is underway.Additionally,a simulation model based on sequences of imperative rules defining behaviors of both regular and compromised nodes is presented.Overall,this technique was experimentally verified using a segment of a WSN embedded in a smart city infrastructure,simulating a wormhole attack.Results demonstrate the viability and practical significance of the technique for enhancing future information security measures.Validation tests confirmed high levels of accuracy and efficiency when applied specifically to detecting wormhole attacks targeting routing protocols in WSNs.Precision and recall rates averaged above the benchmark value of 0.95,thus validating the broad applicability of the proposed models across varied scenarios.
基金supported by National Natural Science Foundation of China(61901071,61871062,61771082,U20A20157)Science and Natural Science Foundation of Chongqing,China(cstc2020jcyjzdxmX0024)+1 种基金University Innovation Research Group of Chongqing(CXQT20017)Scientific and Technological Research Program of Chongqing Municipal Education Commission(No.KJZD-K201901301).
文摘Network virtualization is the development trend and inevitable requirement of hybrid wireless sensor networks(HWSNs).Low mapping efficiency and service interruption caused by mobility seriously affect the reliability of sensing tasks and ultimately affect the long-term revenue of the infrastructure providers.In response to these problems,this paper proposes an efficient virtual network embedding algorithm with a reliable service guarantee.Based on the topological attributes of nodes,a method for evaluating the physical network resource importance degree is proposed,and the nodes with rich resources are selected to improve embedding efficiency.Then,a method for evaluating the physical network reliability degree is proposed to predict the probability of mobile sensors providing uninterrupted services.The simulation results show that the proposed algorithm improves the acceptance rate of virtual sensor networks(VSN)embedding requests and the long-term revenue of the infrastructure providers.
文摘A Wireless Sensor Network(WSN)comprises a series of spatially distributed autonomous devices,each equipped with sophisticated sensors.These sensors play a crucial role in monitoring diverse environmental conditions such as light intensity,air pressure,temperature,humidity,wind,etc.These sensors are generally deployed in harsh and hostile conditions;hence they suffer from different kinds of faults.However,identifying faults in WSN data remains a complex task,as existing fault detection methods,including centralized,distributed,and hybrid approaches,rely on the spatio⁃temporal correlation among sensor nodes.Moreover,existing techniques predominantly leverage classification⁃based machine learning methods to discern the fault state within WSN.In this paper,we propose a regression⁃based bagging method to detect the faults in the network.The proposed bagging method is consisted of GRU(Gated Recurrent Unit)and Prophet model.Bagging allows weak learners to combine efforts to outperform a strong learner,hence it is appropriate to use in WSN.The proposed bagging method was first trained at the base station,then they were deployed at each SN(Sensor Node).Most of the common faults in WSN,such as transient,intermittent and permanent faults,were considered.The validity of the proposed scheme was tested using a trusted online published dataset.Using experimental studies,compared to the latest state⁃of⁃the⁃art machine learning models,the effectiveness of the proposed model is shown for fault detection.Performance evaluation in terms of false positive rate,accuracy,and false alarm rate shows the efficiency of the proposed algorithm.
基金funded by Sardar Vallabhbhai National Institute of Technology through SEED grant No.Dean(R&C)/SEED Money/2021-22/11153Date:08/02/2022supported by Business Finland EWARE-6G project under 6G Bridge program,and in part by theHorizon Europe(Smart Networks and Services Joint Under taking)program under Grant Agreement No.101096838(6G-XR project).
文摘Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time.To address this issue,this paper presents an innovative energy-efficient protocol based on deep Q-learning(DQN),specifically developed to prolong the operational lifespan of WSNs used in border surveillance.By harnessing the adaptive power of DQN,the proposed protocol dynamically adjusts node activity and communication patterns.This approach ensures optimal energy usage while maintaining high coverage,connectivity,and data accuracy.The proposed system is modeled with 100 sensor nodes deployed over a 1000 m×1000 m area,featuring a strategically positioned sink node.Our method outperforms traditional approaches,achieving significant enhancements in network lifetime and energy utilization.Through extensive simulations,it is observed that the network lifetime increases by 9.75%,throughput increases by 8.85%and average delay decreases by 9.45%in comparison to the similar recent protocols.It demonstrates the robustness and efficiency of our protocol in real-world scenarios,highlighting its potential to revolutionize border surveillance operations.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20464,62066005Innovation Project of Guangxi Graduate Education under Grant No.YCSW2024313.
文摘Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.
基金funded by Universiti Putra Malaysia under a Geran Putra Inisiatif(GPI)research grant with reference to GP-GPI/2023/9762100.
文摘This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments.The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency.A novel closed-form expression is also derived for a generalized asymptotic error variance steady state.Steady and convergence analyses are then presented for the synchronization,with frequency adaptations done using least mean square(LMS),the Newton search,the gradient descent(GraDes),the normalized LMS(N-LMS),and the Sign-Data LMS algorithms.Results obtained from real-time experiments showed a better performance of our protocols as compared to the Average Proportional-Integral Synchronization Protocol(AvgPISync)regarding the impact of quantization error on synchronization accuracy,precision,and convergence time.This generalized approach to time synchronization allows flexibility in selecting a suitable protocol for different wireless sensor network applications.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government Ministry of Science and ICT(MIST)(RS-2022-00165225).
文摘Traditional chaotic maps struggle with narrow chaotic ranges and inefficiencies,limiting their use for lightweight,secure image encryption in resource-constrained Wireless Sensor Networks(WSNs).We propose the SPCM,a novel one-dimensional discontinuous chaotic system integrating polynomial and sine functions,leveraging a piecewise function to achieve a broad chaotic range()and a high Lyapunov exponent(5.04).Validated through nine benchmarks,including standard randomness tests,Diehard tests,and Shannon entropy(3.883),SPCM demonstrates superior randomness and high sensitivity to initial conditions.Applied to image encryption,SPCM achieves 0.152582 s(39%faster than some techniques)and 433.42 KB/s throughput(134%higher than some techniques),setting new benchmarks for chaotic map-based methods in WSNs.Chaos-based permutation and exclusive or(XOR)diffusion yield near-zero correlation in encrypted images,ensuring strong resistance to Statistical Attacks(SA)and accurate recovery.SPCM also exhibits a strong avalanche effect(bit difference),making it an efficient,secure solution for WSNs in domains like healthcare and smart cities.
