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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks:A Methodological Survey 认领 引用
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作者 Mohammad Shokouhifar Fakhrosadat Fanian +4 位作者 Mehdi Hosseinzadeh Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期191-255,共65页
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw... Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field. 展开更多
关键词 Wireless sensor networks data transmission energy efficiency lifetime clustering routing optimization metaheuristic algorithms grey wolf optimizer
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Performance Evaluation of Malicious Node Detection and Mitigation of IoT-Based Trust Model for Wireless Sensor Network 认领 引用
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作者 Anil Kumar Abhay Bhatia +3 位作者 Amit Singh Preeti Rani Vincent Omollo Nyangaresi Mahendihasan S.Heera 《Computers, Materials & Continua》 SCIE EI 2026年第7期1921-1947,共27页
The Internet of Things(IoT)enables seamless real-time monitoring and data exchange across distributed and heterogeneous environments with wireless sensor networks(WSNs).The open architecture and resource constraints o... The Internet of Things(IoT)enables seamless real-time monitoring and data exchange across distributed and heterogeneous environments with wireless sensor networks(WSNs).The open architecture and resource constraints of wireless sensor networks(WSNs)make them highly vulnerable to internal security threats caused by malicious or compromised nodes,particularly in Internet of Things(IoT)environments.To address this issue,we proposed Dynamic Trust Evaluation Model(DTEM),designed to provide a secure,scalable,and efficient framework for IoT-based WSNs.The proposed model identifies the role of trust management in routing,data aggregation,and intrusion detection,including trust-based protocols.DTEM incorporates a lightweight elliptic curve cryptography(ECC)mechanism to ensure secure communication,protect trust information from manipulation,and enhance overall system reliability.In addition,machine learning techniques are employed to improve malicious node classification accuracy.Component-wise analysis demonstrates that the dynamic trust evaluation forms the core detection mechanism,while ECC enhances communication security and machine learning improves malicious node classification accuracy.A large-scale network simulation is conducted to evaluate DTEM’s performance under various attack scenarios.Results demonstrate improved malicious node detection accuracy,higher packet delivery ratios,reduced energy consumption,and lower communication overheads.The proposed DTEM framework proves to be a robust and scalable solution for securing IoT-based wireless sensor networks,making it suitable for real-world applications. 展开更多
关键词 Internet of Things node detection security threats security and protocols wireless sensor network
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Quantum-Optimization-Based Clustering and Routing Protocols for Energy-Efficient,Scalable Wireless Sensor Networks 认领 引用
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作者 Amjad Rehman Tariq Mahmood +1 位作者 Faten S.Alamri Muhammad I.Khan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期1352-1394,共43页
The rapid deployment of Wireless Sensor Networks(WSNs)faces critical challenges due to sensor nodes’limited energy and communication capabilities,which restrict network lifetime and data transmission efficiency.Tradi... The rapid deployment of Wireless Sensor Networks(WSNs)faces critical challenges due to sensor nodes’limited energy and communication capabilities,which restrict network lifetime and data transmission efficiency.Traditional clustering and routing protocols often lead to unbalanced energy consumption and uneven load distribution,whereas intelligent optimization approaches are hindered by high computational costs and slow convergence.This research formulates the clustering and routing problems in WSNs as an optimization challenge under resource and energy constraints,aiming to improve stability,energy efficiency,and throughput.This research proposed three quantum optimization-based solutions to address complex issues.First,a Quantum Genetic-Enhanced K-means(QGE-K)protocol addresses inaccurate cluster-head initialization by adaptively determining the optimal number of clusters and selecting energy-balanced cluster heads,thereby improving clustering accuracy and routing efficiency.Second,a Fuzzy-Enhanced Quantum Annealing Algorithm(FEQA)protocol integrates fuzzy inference with quantum tunneling dynamics to select cluster heads and compute the most energy-efficient routing paths,extending the network lifetime in large-scale deployments.Third,a Quantum-Enhanced Particle Swarm Clustering and Routing(QE-PSCR)protocol encodes clustering and routing into a single optimization particle,employing chaotic mapping and Levy flight strategies to accelerate convergence and escape local optima,thereby reducing computation overhead.The simulation results demonstrate that all three protocols achieve significant improvements in energy consumption,load balance,throughput,and overall network lifetime.The proposedmethods apply to domains such as environmental monitoring,the industrial Internet ofThings,and military security,highlighting both theoretical contributions and practical value in advancing energy-efficientWSN design. 展开更多
关键词 Wireless sensor networks quantumgenetic-enhanced K-means quantum annealing algorithm chaotic mapping energy consumption load balancing
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Adaptive Enhanced Grey Wolf Optimizer for Efficient Cluster Head Selection and Network Lifetime Maximization in Wireless Sensor Networks 认领 引用
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作者 Omar Almomani Mahran Al-Zyoud +3 位作者 Ahmad Adel Abu-Shareha Ammar Almomani Said A.Salloum Khaled Mohammad Alomari 《Computers, Materials & Continua》 SCIE EI 2026年第5期784-813,共30页
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ... In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs. 展开更多
关键词 Wireless sensor networks energy efficiency cluster head selection grey wolf optimizer
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A Bilevel Deep Learning Optimization Framework for Joint Energy Harvesting Prediction and Energy-Aware Scheduling in IoT-Based Wireless Sensor Networks 认领 引用
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作者 Mohammad Q.Al-Jamal Mahmoud Al Jamal +3 位作者 Bashar S.Khassawneh Ayoub Alsarhan Amina Salhi Tahani Alsubait 《Computers, Materials & Continua》 SCIE EI 2026年第9期917-944,共28页
Energy sustainability and secure operation are persistent challenges in Internet-of-Things(IoT)wireless sensor networks(WSNs),where limited battery capacity,heterogeneous traffic,and security procedures jointly drive ... Energy sustainability and secure operation are persistent challenges in Internet-of-Things(IoT)wireless sensor networks(WSNs),where limited battery capacity,heterogeneous traffic,and security procedures jointly drive premature node depletion and service degradation.This paper proposes an uncertainty-aware bilevel co-optimization framework that unifies residual-energy prediction with robust,energy-aware scheduling for clustered IoT-WSNs.At the lower level,a lightweight temporal predictor(TCN+LSTM with stochastic sampling)learns short-horizon residual-energy evolution from multivariate,dataset-aligned windows capturing sensing/communication activity,proximity-to-cluster-head effects,and security overhead(authentication latency,key exchange,and rekeying),and produces both point forecasts and uncertainty estimates to enable risk-sensitive control.At the upper level,a constrained,horizon-based scheduler selects per-node actions(duty cycle,sensing rate,transmission power)to extend network lifetime and balance residual energy while enforcing safety thresholds and operational bounds;bilevel coupling is realized via differentiable hypergradient updates,complemented by trust-region action smoothing and adaptive primal–dual constraint handling to suppress energy-critical states under uncertainty.On a real-world WSN energy–security dataset,the proposed model attains the best lower-level learning performance with MAE=0.004,RMSE=0.006,and R²=0.995 for residual-energy regression,and up to 0.98 accuracy/0.98 F1 for secure-and-efficient classification.End-to-end scheduling results show that the full framework improves estimated network lifetime by up to 1.60×,reduces residual-energy variance to 0.60×,and lowers safety violations to 0.35×relative to a fixed-policy baseline,demonstrating robust,secure,and sustainable IoT-enabled WSN operation. 展开更多
关键词 Internet of Things wireless sensor networks bilevel optimization energy-aware scheduling
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CTSO-DRNN:Energy-Aware Delay Prediction and Optimized Data Aggregation in IoT-Based Wireless Sensor Networks 认领 引用
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作者 Reshma Siyal Jun Long +3 位作者 Muhammad Asim Mudasir Ahmad Wani Kashish Ara Shakil Sajid Shah 《Computers, Materials & Continua》 SCIE EI 2026年第7期1808-1823,共16页
The rapid growth of the Internet of Things(IoT)has led to dense wireless sensor networks(WSNs)deployed in critical applications such as smart cities,industrial monitoring,and healthcare.However,energy constraints,unpr... The rapid growth of the Internet of Things(IoT)has led to dense wireless sensor networks(WSNs)deployed in critical applications such as smart cities,industrial monitoring,and healthcare.However,energy constraints,unpredictable communication delays,and inefficient data aggregation remain significant challenges that limit network reliability and operational lifespan.Traditional approaches often fail to balance delay minimization with energy efficiency,especially in large-scale or dynamic networks.To address these issues,this study proposes CTSO-DRNN,a novel framework that integrates Chronological Tangent Search Optimization(CTSO)with a Deep Recurrent Neural Network(DRNN)for accurate delay prediction and optimized data aggregation.The framework constructs Link Delay-Distance(LDD)trees to guide hierarchical communication and leverages CTSO to optimize the DRNN for predicting network delays,enabling adaptive scheduling and energy-aware operation.Experimental findings from simulated WSNs comprising 100 to 250 nodes indicate that the CTSO-DRNN approach decreases the average communication delay by roughly 28%to 60%,increases link lifetime by 8%to 30%,and reduces routing distance by 14%to 25%when compared to various leading-edge techniques across diverse network densities.These improvements highlight the framework’s ability to maintain low latency,prolong network operation,and enhance overall energy efficiency. 展开更多
关键词 Internet of Things(IoT) wireless sensor networks deep recurrent neural network chronological tangent search optimization data aggregation
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Multi-UAV Collaborative Energy Charging for Battery-Free SWIPT-Enabled Sensor Networks Based on MADDPG 认领 引用
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作者 Xiangyi Le Deyu Lin +2 位作者 Yufei Zhao Wang Miao Yong Liang Guan 《Computers, Materials & Continua》 SCIE EI 2026年第9期2376-2395,共20页
The emergence of Unmanned Aerial Vehicle(UAV)-enabled Wireless Energy Transfer(WET)and Simultaneous Wireless Information and Power Transfer(SWIPT)technology provide a promising solution to overcome the energy sustaina... The emergence of Unmanned Aerial Vehicle(UAV)-enabled Wireless Energy Transfer(WET)and Simultaneous Wireless Information and Power Transfer(SWIPT)technology provide a promising solution to overcome the energy sustainability limitations of traditional harvesting-reliant sensor networks.However,in large-scale Battery-free SWIPT-enabled Sensor Networks(BSSN)characterized by sparse node distribution and heterogeneous energy consumption and harvesting rates,employing a single UAV for energy replenishment often suffers from insufficient operation continuity and low charging efficiency.To overcome these challenges,a Multi-UAV Collaborative Energy Charging for BSSN Based on Multi-Agent Deep Deterministic Policy Gradient(MCEC-MADDPG)is proposed in this paper.Specifically,we construct a collaborative one-to-one precision energy supply model where UAVs hover directly above specific nodes to achieve power transmission without complex beamforming requirements.To achieve collaborative scheduling among multiple UAVs in wide-area dynamic environments,the energy replenishment problem is first formulated as a Partially Observable Markov Decision Process(POMDP).Subsequently,the Centralized Training with Decentralized Execution(CTDE)architecture of the MADDPG algorithm is leveraged to solve this POMDP,which effectively tackles the non-stationarity challenge inherent in multi-agent environments.Simulation results demonstrate that MCEC-MADDPG exhibits superior performance in terms of convergence speed and stability.It enables the adaptive emergence of spatial-division collaborative strategies,significantly enhances the average residual energy of the network,and elevates the node survival rate to nearly 90%.Compared with Deep Deterministic Policy Gradient(DDPG),the traditional static Partition-Greedy method,the heuristic K-Means algorithm and the dynamic Two-Layer task allocation strategy,the proposed approach demonstrates substantial advantages. 展开更多
关键词 Battery-free SWIPT-enabled sensor networks multi-agent deep deterministic policy gradient multi-unmanned aerial vehicle collaborative energy charging partially observable Markov decision process centralized training with decentralized execution
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Charging Scheduling of Clustered Wireless Rechargeable Sensor Networks Considering Dynamic Selection of Cluster Heads 认领 引用
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作者 Mengqi Liu Haiqing Yao 《Computers, Materials & Continua》 SCIE EI 2026年第7期1062-1085,共24页
For the wide-coverage application scenarios,wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring,and use the mobile charger(MC)to su... For the wide-coverage application scenarios,wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring,and use the mobile charger(MC)to support the sustainable charging of the network.Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability.However,the existing work tends to use fixed triggering mechanism for cluster head(CH)rotation,and may trigger the rotation either too early or too late.Besides,the existing charging triggering mechanisms cannot track the changes in network topology in real time.As a result,both the network energy efficiency and the node failure rate degenerate correspondingly.To solve these problems,this work proposes a dynamic cluster head selection algorithm(DCHSA),which evaluates potential candidate CH sets based on the energy consumption,remaining energy and topological structure,and then select a new CH within this set based on the CH rotation energy consumption and the candidate CH evaluation mechanism.Furthermore,an adaptive dual-threshold selection algorithm based on dynamic energy consumption(ADTSA-DEC)is proposed to determine the set of requiring charging nodes and the trigger time for charging scheduling.The particle swarm optimization is then employed to implement the charging scheduling.Finally,extensive simulations validate that the newly proposed algorithms have outstanding accuracy and robustness in improving overall network energy efficiency and node survivability compared with existing methods. 展开更多
关键词 Clustered wireless rechargeable sensor networks cluster head rotation adaptive dual-threshold charging scheduling strategy particle swarm optimization
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Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm forWireless Sensor Network Deployment 认领 引用
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作者 Kuo-Chun Tseng I-Chia Chen Yu-Chieh Cho 《Computers, Materials & Continua》 SCIE EI 2026年第9期2413-2448,共36页
Wireless Sensor Networks(WSNs)are important infrastructure for smart-city applications,such as environmental monitoring,public safety,and smart transportation.However,finding effective sensor locations is an NP-hard p... Wireless Sensor Networks(WSNs)are important infrastructure for smart-city applications,such as environmental monitoring,public safety,and smart transportation.However,finding effective sensor locations is an NP-hard problem because a deployment must satisfy sensing coverage and communication connectivity while minimizing the number of sensors.Following the basic framework of a previous study,this study replaces the original optimization algorithmwith the Amplitude-Ensemble Quantum-inspired Tabu Search(AEQTS)algorithmand retains the same entanglement-like initialization strategy,resulting in the proposed AEQTSwE(AEQTS with Entanglement)framework for theWSNdeployment problem.AEQTSwE uses a quantum-inspired search mechanism and an ensemble update strategy to explore the solution space more efficiently,while the retained initialization strategy provides highquality initial deployments.Experimental results show that AEQTSwE reduces the number of deployed sensors while satisfying the required coverage and connectivity constraints.It also converges faster and producesmore stable solutions than existing approaches under different conditions.Sensitivity,ablation,statistical,and complexity analyses further show that AEQTSwE has low parameter sensitivity,stable performance,and potential for larger and more complex deployment scenarios. 展开更多
关键词 Wireless sensor network deployment sensor deployment quantum-inspired optimization smart-city applications
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An Energy-Efficient and Reliability-Aware Climate-Conscious Clustered Routing Framework for Sustainable Ocean Observation in Underwater Wireless Sensor Networks 认领 引用
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作者 Kiran Saleem Upinder Kaur +4 位作者 Abdulrahman Mohammed Alamoudi Mai Alduailij Ahmad Subhi Salem Mufleh Ateeq Ur Rehman Salil Bharany 《Computers, Materials & Continua》 SCIE EI 2026年第10期1531-1552,共22页
Underwater Wireless Sensor Networks(UWSNs)are exceedingly critical for large-scale underwater applications,such as environmental monitoring,infrastructure inspection,target tracking,and marine surveillance.Nevertheles... Underwater Wireless Sensor Networks(UWSNs)are exceedingly critical for large-scale underwater applications,such as environmental monitoring,infrastructure inspection,target tracking,and marine surveillance.Nevertheless,network lifetime and communication reliability are severely constrained by harsh underwater acoustic conditions,limited battery power,large propagation delays,node mobility,and uneven energy consumption.In response to these issues,this study proposes a Climate-Aware Hybrid Clustering and Routing(CA-HCR-UWSN)framework to enable sustainable,long-term underwater monitoring.This work proposes a hybrid framework that combines Elephant Herding Optimization(EHO)with the Gravitational Search Algorithm(GSA)to provide an effective solution to these challenges.To minimize unnecessary transmissions and energy usage within the clusters,a chain-oriented data aggregation mechanism based on Chain-Oriented Sensor Network(COSEN)is used,with the parameters of the climate and the underwater acoustic channel clearly taken into consideration.Moreover,a reliability-conscious inter-cluster routing policy that accounts for signal-to-noise ratio,packet error rate,and link reliability is also established to ensure reliable data delivery in a dynamic underwater environment.Extensive simulation results indicate that CA-HCR-UWSN consistently outperforms state-of-the-art protocols,including FCMMFO,MCR-UWSN,EE-UWSN,WDFAD-DBR,and EESLEPRP.The proposed framework achieves an 18%-25%increase in the network’s common lifetime,a 15%-20%increase in packet delivery,20%-26%energy savings,and a 17%-22%decrease in end-to-end delay compared with existing methods,along with better load balancing and network stability.These findings verify that CA-HCR-UWSN is a strong,scalable,and energy-efficient solution for long-term,climate-conscious underwater sensing applications. 展开更多
关键词 Underwater wireless sensor networks(UWSNs) climate-aware routing energy-efficient communication reliability-aware networking ocean monitoring systems IoT-enabled marine sensing sustainable digital ocean
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Efficient Profit-Aware UAV Charging Strategy in Wireless Sensor Networks 认领 引用
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作者 Xin Yuanxue Ning Yue Shi Pengfei 《China Communications》 SCIE EI CSCD 2026年第6期325-336,共12页
With the development of internet of things(IoT)technology,sensor networks are used in a wide variety of fields.However,the energy replenishment of sensor nodes(SNs)is still a challenge.In this paper,we propose an unma... With the development of internet of things(IoT)technology,sensor networks are used in a wide variety of fields.However,the energy replenishment of sensor nodes(SNs)is still a challenge.In this paper,we propose an unmanned aerial vehicle(UAV)wireless charging strategy(WCS),which can jointly maximize the system profit and the number of survival nodes through an efficient charging order.To effectively reduce the data transmission delay,we group the nodes to realize the data collection solely by cluster head(CH).In the proposed charging order algorithm,we particularly design a CH replacement scheme when the original head runs out of energy before the UAV arrives.Compared with other UAV charging schemes,the proposed WCS shows a better performance in terms of system profit and time delay.Furthermore,it ensures a rather satisfying node survival rate. 展开更多
关键词 charging profit energy management internet of things UAV communications wireless sensor network
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Feasibility-Aware Reinforcement Learning for Reliable Hop-Constrained Routing in Wireless Sensor Networks 认领 引用
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作者 Adeel Iqbal Muhammad Faisal Siddiqui 《Computers, Materials & Continua》 SCIE EI 2026年第10期2186-2204,共19页
Hop-constrained packet routing is a fundamental problem in wireless sensor networks(WSNs),where latency constraints,energy limitations,and practical feasibility requirements greatly restrict routing choices.Traditiona... Hop-constrained packet routing is a fundamental problem in wireless sensor networks(WSNs),where latency constraints,energy limitations,and practical feasibility requirements greatly restrict routing choices.Traditional methods based on shortest path and greedy routing have low complexity but cannot adapt to dynamic network changes well,while reinforcement learning for routing has the potential to adapt to network variations but has not been well explored in the hard hop-constrained setting.The current study attempts to fill the gap by modeling hop-constrained routing as the decision-making problem in a finite-horizon setting.An integrated simulation environment is proposed that unifies the concept of feasibility-aware action masking,energy-and trust-aware routing mechanisms,and simulation-related evaluation criteria.In this unified environment,four representative reinforcement learning methods,REINFORCE,Advantage Actor-Critic(A2C),Proximal Policy Optimization(PPO),and Deep Q-Network(DQN),are applied and validated against greedy forwarding,shortest-path routing,and Dijkstra routing under strict(H=5)and relaxed(H=15)hop limits using multi-seed testing.Under tight hop constraints,Dijkstra achieves a delivery success rate of 1.000,while greedy routing reaches 0.950±0.014.Among the learning algorithms,PPO,A2C,and DQN reach approximately 0.945±0.014 at their best checkpoints with near-baseline hop efficiency,indicated by an average hop count of about 4.34±0.04.Under relaxed hop constraints,Dijkstra remains at 1.000,greedy forwarding reaches 0.984±0.008,and PPO,A2C,and DQN achieve high best-checkpoint success rates of approximately 0.991-0.992.REINFORCE improves under the relaxed setting but remains less stable than the stronger learned policies.The experiments show that feasibility-aware learning can approach deterministic baseline reliability while retaining learned forwarding capability under hop constraints.The ablation results further confirm that action masking is the dominant mechanism for maintaining feasible routing decisions,whereas trust mainly provides reliability-aware regularization.These observations emphasize the need to evaluate RL-based routing using deployment-level metrics,including success probability,hop-count distribution,invalid-action rate,route-risk rate,and return,rather than relying only on training reward. 展开更多
关键词 Wireless sensor networks reinforcement learning routing protocols hop-constrained routing reliability-aware routing
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Hierarchical detection and tracking for moving targets in underwater wireless sensor networks 认领 引用 被引量：2
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作者 Yudong Li Hongcheng Zhuang +2 位作者 Long Xu Shengquan Li Haibo Lu 《Digital Communications and Networks》 SCIE EI CSCD 2025年第2期556-562,共7页
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
关键词 Underwater wireless sensor networks The degree of target change Mutual information Pheromone Adaptive period
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Wireless Sensor Network Modeling and Analysis for Attack Detection 认领 引用 被引量：1
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作者 Tamara Zhukabayeva Vasily Desnitsky Assel Abdildayeva 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期2591-2625,共35页
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
关键词 Wireless sensor network modeling security attack detection monitoring
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APFed: Adaptive personalized federated learning for intrusion detection in maritime meteorological sensor networks 认领 引用 被引量：1
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作者 Xin Su Guifu Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2025年第2期401-411,共11页
With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Marit... With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios. 展开更多
关键词 Intrusion detection Maritime meteorological sensor network Federated learning Personalized model Deep learning
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A Hybrid Framework Integrating Deterministic Clustering,Neural Networks,and Energy-Aware Routing for Enhanced Efficiency and Longevity in Wireless Sensor Network 认领 引用 被引量：2
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作者 Muhammad Salman Qamar Muhammad Fahad Munir 《Computers, Materials & Continua》 SCIE EI 2025年第9期5463-5485,共23页
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
关键词 Wireless sensor networks(WSNs) machine learning based artificial neural networks(ANNs) energy consumption(EC) LEACH-D sensor nodes(SNs) Bayesian Regularization Algorithm(BRA)
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A Comprehensive Review on Urban Resilience via Fault-Tolerant IoT and Sensor Networks 认领 引用 被引量：2
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作者 Hitesh Mohapatra 《Computers, Materials & Continua》 SCIE EI 2025年第10期221-247,共27页
Fault tolerance is essential for reliable and sustainable smart city infrastructure.Interconnected IoT systems must function under frequent faults,limited resources,and complex conditions.Existing research covers vari... Fault tolerance is essential for reliable and sustainable smart city infrastructure.Interconnected IoT systems must function under frequent faults,limited resources,and complex conditions.Existing research covers various fault-tolerant methods.However,current reviews often lack system-level critique and multidimensional analysis.This study provides a structured review of fault tolerance strategies across layered IoT architectures in smart cities.It evaluates fault detection,containment,and recovery techniques using specific metrics.These include fault visibility,propagation depth,containment score,and energy-resilience trade-offs.The analysis uses comparative tables,architecture-aware discussions,and conceptual plots.It investigates the impact of fault tolerance on decision-making in Supervisory Control And Data Acquisition(SCADA)systems,sensor networks,and real-time controllers.Simulation results and logic-based design support the relationships between evaluation metrics.Findings show a common reliance on redundancy and reactive methods.Many techniques fail to address cross-layer propagation,context-aware adaptation,and silent fault impact on user trust.The study combines these overlooked aspects into a system-level framework.This survey identifies performance bottlenecks and supports the design of adaptive,energy-efficient,and transparent IoT systems.The results contribute to bridging technical reliability with public trust,supporting scalable and responsible smart city development. 展开更多
关键词 Smart cities sensor networks fault-tolerance urban systems resilient infrastructure Internet of Things(IoT)in cities
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Data Gathering Based on Hybrid Energy Efficient Clustering Algorithm and DCRNN Model in Wireless Sensor Network 认领 引用
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作者 Li Cuiran Liu Shuqi +1 位作者 Xie Jianli Liu Li 《China Communications》 SCIE EI CSCD 2025年第3期115-131,共17页
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
关键词 clustering data gathering DCRNN model network lifetime wireless sensor network
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A Recursive Method to Encryption-Decryption-Based Distributed Set-Membership Filtering for Time-Varying Saturated Systems Over Sensor Networks 认领 引用
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作者 Jun Hu Jiaxing Li +2 位作者 Chaoqing Jia Xiaojian Yi Hongjian Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期1047-1049,共3页
Dear Editor,This letter deals with the distributed recursive set-membership filtering(DRSMF)issue for state-saturated systems under encryption-decryption mechanism.To guarantee the data security,the encryption-decrypt... Dear Editor,This letter deals with the distributed recursive set-membership filtering(DRSMF)issue for state-saturated systems under encryption-decryption mechanism.To guarantee the data security,the encryption-decryption mechanism is considered in the signal transmission process.Specifically,a novel DRSMF scheme is developed such that,for both state saturation and encryption-decryption mechanism,the filtering error(FE)is limited to the ellipsoid domain.Then,the filtering error constraint matrix(FECM)is computed and a desirable filter gain is derived by minimizing the FECM.Besides,the bound-edness evaluation of the FECM is provided. 展开更多
关键词 time varying saturated systems signal transmission processspecificallya encryption decryption mechanism sensor networks recursive method distributed set membership filtering
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