Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caus...Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.展开更多
Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand...Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.展开更多
Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization...Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.展开更多
Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the lim...Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
A series of Sn microalloying high-strength low-alloy(HSLA)steels were prepared through vacuum melting and hot rolling.Their stress corrosion cracking(SCC)behavior under high Cl^(−)environments was investigated using U...A series of Sn microalloying high-strength low-alloy(HSLA)steels were prepared through vacuum melting and hot rolling.Their stress corrosion cracking(SCC)behavior under high Cl^(−)environments was investigated using U-bend immersion,slow strain rate testing,electrochemical methods,and novel SCC sensor.Results revealed that HSLA steel microalloying with 0.1 wt.%Sn demonstrated superior SCC resistance,primarily attributed to the effective inhibition of the anodic dissolution mechanism.Fracture morphology revealed a transformation in fracture mode from brittle to a mixture of brittle-ductile characteristics,accompanied by the formation of a protective SnO_(2)oxide film on the steel surface.However,excessive Sn content exacerbated SCC susceptibility due to the increased hydrolysis of Sn^(2+),leading to localized pitting and crack initiation.The critical role of optimal Sn content was highlighted in balancing mechanical properties and corrosion resistance,suggesting potential applications in industries where materials face harsh chloride environments.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
Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade...Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade substantially under malicious interference.We introduce iSTSP,an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust,precise synchronization even in hostile environments:(1)trust preprocessing that filters node participation using behavioral trust scoring;(2)anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time;(3)reliability-weighted consensus that prioritizes high-trust nodes during time aggregation;and(4)convergence-optimized synchronization that dynamically adjusts parameters using theoretical stability bounds.We provide rigorous convergence analysis including a closed-form expression for convergence time,and validate the protocol through both simulations and realworld experiments on a controlled 16-node testbed.Under Sybil attacks with five malicious nodes within this testbed,iSTSP maintains synchronization error increases under 12%and achieves a rapid convergence.Compared to state-ofthe-art protocols like TPSN,SE-FTSP,and MMAR-CTS,iSTSP offers 60%faster detection,broader threat coverage,and more than 7 times lower synchronization error,with a modest 9.3%energy overhead over 8 h.We argue this is an acceptable trade-off for mission-critical deployments requiring guaranteed security.These findings demonstrate iSTSP’s potential as a reliable solution for secure WSN synchronization and motivate future work on large-scale IoT deployments and integration with energy-efficient communication protocols.展开更多
Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio fre...Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio frequency （RF） parameters and various microcontroller unit （MCU） solutions. An implementation of the WSN node is presented with experimental results and a novel ＂one processor working at two frequencies＂ energy saving strategy. The lifetime estimation issue is analyzed with consideration to the periodical listen required by common WSN media access control （MAC） algorithms. It can be concluded that the startup time of the RF which determines the best sleep time ratio and the shortest backoff slot time of MAC, the RF frequency and modulation methods which determinate the RX and TX current, and the overall energy consumption of the dual frequency MCU SOC （ system on chip） are the most essential factors for the WSN node physical layer design.展开更多
基金Supported by 2021 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Series Course Teaching Team(PPJH202102JXTD)2022 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Engineering(PPJHKCSZ-2022301)+1 种基金2023 Zhanjiang Science and Technology Bureau Project:Design and Simulation of Zhanjiang Mangrove Wetland Monitoring Network System(2023B01017)2022 Zhanjiang University of Science and Technology Quality Engineering Project:Audiovisual Language Teaching and Research Office(ZLGC202203).
文摘Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
基金the VNUHCM-University of Information Technology’s Scientific Research Support Fund.
文摘Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios.
基金appreciation to King Saud University for funding this research through the Researchers Supporting Program number(RSPD2024R918),King Saud University,Riyadh,Saudi Arabia.
文摘Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.
文摘Wireless Sensor Networks(WSNs)have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes(SNs).However,the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs.Current energy efficiency strategies,such as clustering,multi-hop routing,and data aggregation,face challenges,including uneven energy depletion,high computational demands,and suboptimal cluster head(CH)selection.To address these limitations,this paper proposes a hybrid methodology that optimizes energy consumption(EC)while maintaining network performance.The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic(LEACH-D)protocol using an Artificial Neural Network(ANN)and Bayesian Regularization Algorithm(BRA).LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs,mitigating inefficiencies from random CH selection.The ANN further enhances CH selection and routing processes,effectively reducing data transmission overhead and idle listening.Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols.This framework offers a promising solution to the energy efficiency challenges in WSNs,paving the way for more sustainable and reliable network deployments.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金support of the National Natural Science Foundation of China(No.52171063).
文摘A series of Sn microalloying high-strength low-alloy(HSLA)steels were prepared through vacuum melting and hot rolling.Their stress corrosion cracking(SCC)behavior under high Cl^(−)environments was investigated using U-bend immersion,slow strain rate testing,electrochemical methods,and novel SCC sensor.Results revealed that HSLA steel microalloying with 0.1 wt.%Sn demonstrated superior SCC resistance,primarily attributed to the effective inhibition of the anodic dissolution mechanism.Fracture morphology revealed a transformation in fracture mode from brittle to a mixture of brittle-ductile characteristics,accompanied by the formation of a protective SnO_(2)oxide film on the steel surface.However,excessive Sn content exacerbated SCC susceptibility due to the increased hydrolysis of Sn^(2+),leading to localized pitting and crack initiation.The critical role of optimal Sn content was highlighted in balancing mechanical properties and corrosion resistance,suggesting potential applications in industries where materials face harsh chloride environments.
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this project under Geran Putra Inisiatif(GPI)with reference of GP-GPI/2023/976210。
文摘Accurate time synchronization is fundamental to the correct and efficient operation of Wireless Sensor Networks(WSNs),especially in security-critical,time-sensitive applications.However,most existing protocols degrade substantially under malicious interference.We introduce iSTSP,an Intelligent and Secure Time Synchronization Protocol that implements a four-stage defense pipeline to ensure robust,precise synchronization even in hostile environments:(1)trust preprocessing that filters node participation using behavioral trust scoring;(2)anomaly isolation employing a lightweight autoencoder to detect and excise malicious nodes in real time;(3)reliability-weighted consensus that prioritizes high-trust nodes during time aggregation;and(4)convergence-optimized synchronization that dynamically adjusts parameters using theoretical stability bounds.We provide rigorous convergence analysis including a closed-form expression for convergence time,and validate the protocol through both simulations and realworld experiments on a controlled 16-node testbed.Under Sybil attacks with five malicious nodes within this testbed,iSTSP maintains synchronization error increases under 12%and achieves a rapid convergence.Compared to state-ofthe-art protocols like TPSN,SE-FTSP,and MMAR-CTS,iSTSP offers 60%faster detection,broader threat coverage,and more than 7 times lower synchronization error,with a modest 9.3%energy overhead over 8 h.We argue this is an acceptable trade-off for mission-critical deployments requiring guaranteed security.These findings demonstrate iSTSP’s potential as a reliable solution for secure WSN synchronization and motivate future work on large-scale IoT deployments and integration with energy-efficient communication protocols.
基金The National High Technology Research and Deve-lopment Program of China (863Program) (No.2003AA143040).
文摘Major consideration dimensions for the physical layer design of wireless sensor network （WSN） nodes is analyzed by comparing different wireless communication approaches, diverse mature standards, important radio frequency （RF） parameters and various microcontroller unit （MCU） solutions. An implementation of the WSN node is presented with experimental results and a novel ＂one processor working at two frequencies＂ energy saving strategy. The lifetime estimation issue is analyzed with consideration to the periodical listen required by common WSN media access control （MAC） algorithms. It can be concluded that the startup time of the RF which determines the best sleep time ratio and the shortest backoff slot time of MAC, the RF frequency and modulation methods which determinate the RX and TX current, and the overall energy consumption of the dual frequency MCU SOC （ system on chip） are the most essential factors for the WSN node physical layer design.
