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
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
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
Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,whi...Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.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
A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many up...A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.展开更多
Recent developments in wireless communication and embedded computing technologies have led to the advent of wireless sensor network technology. Hundreds of thousands of these micro sensors can be deployed in many area...Recent developments in wireless communication and embedded computing technologies have led to the advent of wireless sensor network technology. Hundreds of thousands of these micro sensors can be deployed in many areas including health, environment and battlefield in order to monitor the domain with desired level of accuracy. When wireless sensors are deployed in an area, the lifetime of the network should last as long as possible according to the original amount of energy. Therefore, reducing energy consumption in WSNs is of primary concern. We have proposed a node scheduling solution that solves the coverage and connectivity problem in sensor networks in an integrated manner. In this way we will divide network life time to finite number of rounds and in each round we will generate a coverage bitmap of sensors of the domain and based on this bitmap it will decided which sensors remain active or go to sleep. We will check the connection of the sensor network by using Laplacian of adjancy graph of active nodes in each round. Also the network will be capable of producing desired percentage of coverage by using coverage bitmap. We will define the connected coverage problem as an optimization problem and we will seek a solution for the problem by using Genetic Algorithm optimization method.展开更多
In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of c...In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.展开更多
One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monito...One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.展开更多
A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the...A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.展开更多
Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the di...Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the difference between the barrier coverage and blanket coverage. In this paper, we propose two algorithms, namely, local tree based no-way and back (LTNWB) algorithm and sensor minimum cut sets (SMCS) algorithm, for the opened and closed belt regions to determine the degree of the barrier coverage of WSN. Our main objective is to minimize the complexity of these algorithms. For the opened belt region, both algorithms work well, and for the closed belt region, they will still come into existence while some restricted conditions are taken into consideration. Finally, the simulation results demonstrate the feasibility of the proposed algorithms.展开更多
Wireless sensor networks can be used to monitor the interested region by deploying dense sensor nodes. Coverage is a primary metric to evaluate the capacity of monitoring. In this paper, we focus on the coverage probl...Wireless sensor networks can be used to monitor the interested region by deploying dense sensor nodes. Coverage is a primary metric to evaluate the capacity of monitoring. In this paper, we focus on the coverage problem under border effects, where the sensor nodes are distributed in a circle-shaped region randomly. Under this scenario, we derive the expected coverage of the sensor node and the total network coverage provided by n sensor nodes accurately by probability. These findings are useful to determine the related parameters （sensing range, number of sensor nodes and radius of monitored region） for a specific network coverage ratio. Simulation results demonstrate that our analysis is correct and effective.展开更多
Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under dep...Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.展开更多
Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is base...Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.展开更多
Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large ext...Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-efficient Area Coverage protocol for Heterogeneous Energy sensor networks (ACHE) is proposed. ACHE can achieve a good performance in terms of sensing area coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, ACHE selects the active nodes based on the average residual energy of neighboring nodes and its own residual energy parameters. Our simulation demonstrates that ACHE not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, ACHE can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.展开更多
A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as...A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.展开更多
Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monito...Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.展开更多
Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The ...Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The existing approaches (often RSSI measurement-based), however, suffer from heavy measurement cost and are not well suitable for the large-scale densely deployed WSNs. NRC-Map, a novel algorithm is put forward for sensor nodes radio coverage mapping. The algorithm is based on the RSSI values collected by the neighbor nodes. According to the spatial relationship, neighbor nodes are mapping to several overlapped sectors. By use of the least squares fitting method, a log-distance path loss model is established for each sector. Then, the max radius of each sector is computed according to the path loss model and the given signal attenuation threshold. Finally, all the sectors are overlapped to estimate the node radio coverage. Experimental results show that the method is simple and effectively improve the prediction accuracy of the sensor node radio coverage.展开更多
In order to make up for the deficiencies and insufficiencies that In order to make up for the deficiencies and insufficiencies that wireless sensor network is constituted absolutely by static or dynamic sensor nodes. ...In order to make up for the deficiencies and insufficiencies that In order to make up for the deficiencies and insufficiencies that wireless sensor network is constituted absolutely by static or dynamic sensor nodes. So a deployment mechanism for hybrid nodes barrier coverage (HNBC)is proposed in wireless sensor network, which collaboratively consists of static and dynamic sensor nodes. We introduced the Voronoi diagram to divide the whole deployment area. According to the principle of least square method, and the static nodes are used to construct the reference barrier line (RBL). And we implemented effectively barrier coverage by monitoring whether there is a coverage hole in the deployment area, and then to determine whether dynamic nodes need limited mobility to redeploy the monitoring area. The simulation results show that the proposed algorithm improved the coverage quality, and completed the barrier coverage with less node moving distance and lower energy consumption, and achieved the expected coverage requirements展开更多
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 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.
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This research work was supported by the National Natural Science Foundation of China(61772454,61811530332).Professor Gwang-jun Kim is the corresponding author.
文摘Wireless Sensor Networks(WSNs)are large-scale and high-density networks that typically have coverage area overlap.In addition,a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area,which leads to coverage holes in WSNs.Thus,coverage control plays an important role in WSNs.To alleviate unnecessary energy wastage and improve network performance,we consider both energy efficiency and coverage rate for WSNs.In this paper,we present a novel coverage control algorithm based on Particle Swarm Optimization(PSO).Firstly,the sensor nodes are randomly deployed in a target area and remain static after deployment.Then,the whole network is partitioned into grids,and we calculate each grid’s coverage rate and energy consumption.Finally,each sensor nodes’sensing radius is adjusted according to the coverage rate and energy consumption of each grid.Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption.
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supported by National Basic Research Program of China (No. 2010CB731800)National Natural Science Foundation of China (No. 60934003)Educational Foundation of Hebei Province (No. 2008147)
文摘A common and critical operation for wireless sensor networks is data gathering. The efficient clustering of a sensor network that can save energy and improve coverage efficiency is an important requirement for many upper layer network functions. This study concentrates on how to form clusters with high uniformity while prolonging the network lifetime. A novel clustering scheme named power- and coverage- aware clustering （PCC） is proposed, which can adaptively select cluster heads according to a hybrid of the nodesI residual energy and loyalty degree. Additionally, the PCC scheme is independent of node distribution or density, and it is free of node hardware limitations, such as self-locating capability and time synchronization. Experiment results show that the scheme performs well in terms of cluster size （and its standard deviation）, number of nodes alive over time, total energy consumption, etc.
文摘Recent developments in wireless communication and embedded computing technologies have led to the advent of wireless sensor network technology. Hundreds of thousands of these micro sensors can be deployed in many areas including health, environment and battlefield in order to monitor the domain with desired level of accuracy. When wireless sensors are deployed in an area, the lifetime of the network should last as long as possible according to the original amount of energy. Therefore, reducing energy consumption in WSNs is of primary concern. We have proposed a node scheduling solution that solves the coverage and connectivity problem in sensor networks in an integrated manner. In this way we will divide network life time to finite number of rounds and in each round we will generate a coverage bitmap of sensors of the domain and based on this bitmap it will decided which sensors remain active or go to sleep. We will check the connection of the sensor network by using Laplacian of adjancy graph of active nodes in each round. Also the network will be capable of producing desired percentage of coverage by using coverage bitmap. We will define the connected coverage problem as an optimization problem and we will seek a solution for the problem by using Genetic Algorithm optimization method.
基金National Natural Science Foundations of China (No.61073177,60905037)
文摘In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.
基金supported by the Islamic Azad University Urmia Brach,Iran
文摘One way to reduce energy consumption in wireless sensor networks is to reduce the number of active nodes in the network. When sensors are redundantly deployed, a subset of sensors should be selected to actively monitor the field （referred to as a ＂cover＂）, whereas the rest of the sensors should be put to sleep to conserve their batteries. In this paper, a learning automata based algorithm for energy-efficient monitoring in wireless sensor networks （EEMLA） is proposed. Each node in EEMLA algorithm is equipped with a learning automaton which decides for the node to be active or not at any time during the operation of the network. Using feedback received from neighboring nodes, each node gradually learns its proper state during the operation of the network. Experimental results have shown that the proposed monitoring algorithm in comparison to other existing methods such as Tian and LUC can better prolong the network lifetime.
基金Project(2008BA00400)supported by the Foundation of Department of Science and Technology of Jiangxi Province,China
文摘A novel immune-swarm intelligence (ISI) based algorithm for solving the deterministic coverage problems of wireless sensor networks was presented.It makes full use of information sharing and retains diversity from the principle of particle swarm optimization (PSO) and artificial immune system (AIS).The algorithm was analyzed in detail and proper swarm size,evolving generations,gene-exchange individual order,and gene-exchange proportion in molecule were obtained for better algorithm performances.According to the test results,the appropriate parameters are about 50 swarm individuals,over 3 000 evolving generations,20%-25% gene-exchange proportion in molecule with gene-exchange taking place between better fitness affinity individuals.The algorithm is practical and effective in maximizing the coverage probability with given number of sensors and minimizing sensor numbers with required coverage probability in sensor placement.It can reach a better result quickly,especially with the proper calculation parameters.
文摘Recently, the barrier coverage was proposed and received much attention in wireless sensor network (WSN), and the degree of the barrier coverage, one of the critical parameters of WSN, must be re-studied due to the difference between the barrier coverage and blanket coverage. In this paper, we propose two algorithms, namely, local tree based no-way and back (LTNWB) algorithm and sensor minimum cut sets (SMCS) algorithm, for the opened and closed belt regions to determine the degree of the barrier coverage of WSN. Our main objective is to minimize the complexity of these algorithms. For the opened belt region, both algorithms work well, and for the closed belt region, they will still come into existence while some restricted conditions are taken into consideration. Finally, the simulation results demonstrate the feasibility of the proposed algorithms.
基金supported in part by National Basic Research Program of China(973 Program)(2010CB731803)National Natural Science Foundation of China(61375105)+2 种基金China Postdoctoral Science Foundation Funded Project(2015M570235)Youth Foundation of Hebei Educational Committee(QN2015187)Science Foundation of Yanshan University(B832,14LGA010)
基金the National Natural Science Foundation of China(No.60473001,60572037)
文摘Wireless sensor networks can be used to monitor the interested region by deploying dense sensor nodes. Coverage is a primary metric to evaluate the capacity of monitoring. In this paper, we focus on the coverage problem under border effects, where the sensor nodes are distributed in a circle-shaped region randomly. Under this scenario, we derive the expected coverage of the sensor node and the total network coverage provided by n sensor nodes accurately by probability. These findings are useful to determine the related parameters （sensing range, number of sensor nodes and radius of monitored region） for a specific network coverage ratio. Simulation results demonstrate that our analysis is correct and effective.
文摘Wireless Sensor Networks(WSNs)play an indispensable role in the lives of human beings in the fields of environment monitoring,manufacturing,education,agriculture etc.,However,the batteries in the sensor node under deployment in an unattended or remote area cannot be replaced because of their wireless existence.In this context,several researchers have contributed diversified number of cluster-based routing schemes that concentrate on the objective of extending node survival time.However,there still exists a room for improvement in Cluster Head(CH)selection based on the integration of critical parameters.The meta-heuristic methods that concentrate on guaranteeing both CH selection and data transmission for improving optimal network performance are predominant.In this paper,a hybrid Marine Predators Optimization and Improved Particle Swarm Optimizationbased Optimal Cluster Routing(MPO-IPSO-OCR)is proposed for ensuring both efficient CH selection and data transmission.The robust characteristic of MPOA is used in optimized CH selection,while improved PSO is used for determining the optimized route to ensure sink mobility.In specific,a strategy of position update is included in the improved PSO for enhancing the global searching efficiency of MPOA.The high-speed ratio,unit speed rate and low speed rate strategy inherited by MPOA facilitate better exploitation by preventing solution from being struck into local optimality point.The simulation investigation and statistical results confirm that the proposed MPOIPSO-OCR is capable of improving the energy stability by 21.28%,prolonging network lifetime by 18.62%and offering maximum throughput by 16.79%when compared to the benchmarked cluster-based routing schemes.
基金supported by the National Nature Science Foundation of China (61170169, 61170168)
文摘Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.
文摘Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-efficient Area Coverage protocol for Heterogeneous Energy sensor networks (ACHE) is proposed. ACHE can achieve a good performance in terms of sensing area coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, ACHE selects the active nodes based on the average residual energy of neighboring nodes and its own residual energy parameters. Our simulation demonstrates that ACHE not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, ACHE can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.
文摘A wireless sensor network (WSN) is spatially distributing independent sensors to monitor physical and environmental characteristics such as temperature, sound, pressure and also provides different applications such as battlefield inspection and biological detection. The Constrained Motion and Sensor (CMS) Model represents the features and explain k-step reach ability testing to describe the states. The description and calculation based on CMS model does not solve the problem in mobile robots. The ADD framework based on monitoring radio measurements creates a threshold. But the methods are not effective in dynamic coverage of complex environment. In this paper, a Localized Coverage based on Shape and Area Detection (LCSAD) Framework is developed to increase the dynamic coverage using mobile robots. To facilitate the measurement in mobile robots, two algorithms are designed to identify the coverage area, (i.e.,) the area of a coverage hole or not. The two algorithms are Localized Geometric Voronoi Hexagon (LGVH) and Acquaintance Area Hexagon (AAH). LGVH senses all the shapes and it is simple to show all the boundary area nodes. AAH based algorithm simply takes directional information by locating the area of local and global convex points of coverage area. Both these algorithms are applied to WSN of random topologies. The simulation result shows that the proposed LCSAD framework attains minimal energy utilization, lesser waiting time, and also achieves higher scalability, throughput, delivery rate and 8% maximal coverage connectivity in sensor network compared to state-of-art works.
文摘Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply coverage.The study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of IWSN.The hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN system.The model improves the power supply to a more considerable extent with the least number of power bank deployments.The main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic algorithm.An overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or areas.Bresenham’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment problems.Further,the cellular automata(CA)is utilized to enhance the convergence speed.The case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization process.Comparative experiments are conducted on four benchmark functions to validate the effectiveness of the proposed method.The experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)algorithms.The results show that the proposed energy coverage optimization method based on the hybrid power bank deployment model generates more accurate results than the results obtained by similar algorithms(i.e.,ABC,GABC).The proposed model is,therefore,effective and efficient for optimization in the IWSN.
文摘Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The existing approaches (often RSSI measurement-based), however, suffer from heavy measurement cost and are not well suitable for the large-scale densely deployed WSNs. NRC-Map, a novel algorithm is put forward for sensor nodes radio coverage mapping. The algorithm is based on the RSSI values collected by the neighbor nodes. According to the spatial relationship, neighbor nodes are mapping to several overlapped sectors. By use of the least squares fitting method, a log-distance path loss model is established for each sector. Then, the max radius of each sector is computed according to the path loss model and the given signal attenuation threshold. Finally, all the sectors are overlapped to estimate the node radio coverage. Experimental results show that the method is simple and effectively improve the prediction accuracy of the sensor node radio coverage.
文摘In order to make up for the deficiencies and insufficiencies that In order to make up for the deficiencies and insufficiencies that wireless sensor network is constituted absolutely by static or dynamic sensor nodes. So a deployment mechanism for hybrid nodes barrier coverage (HNBC)is proposed in wireless sensor network, which collaboratively consists of static and dynamic sensor nodes. We introduced the Voronoi diagram to divide the whole deployment area. According to the principle of least square method, and the static nodes are used to construct the reference barrier line (RBL). And we implemented effectively barrier coverage by monitoring whether there is a coverage hole in the deployment area, and then to determine whether dynamic nodes need limited mobility to redeploy the monitoring area. The simulation results show that the proposed algorithm improved the coverage quality, and completed the barrier coverage with less node moving distance and lower energy consumption, and achieved the expected coverage requirements
