Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.T...Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.展开更多
The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment e...The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment environment puts higher requirements on the search space,computational cost,and optimization efficiency of the algorithms.For this reason,a slime mould algorithm called SCA-SMA is proposed to solve the above problem.In SCA-SMA,a reverse Sobol sequence is used to initialize the population to increase the population diversity and improve the probability of approaching the optimal solution.To better balance local exploitation and global exploration,a dynamic selection of sine cosine update mechanism is proposed:using an optimal position selection mechanism in the global exploration phase to avoid local optima,and integrating the sine cosine algorithm in the local exploitation phase to improve the mucilage position update method,enrich the optimization search process and enhance the development capability of the algorithm.Finally,an adaptive mutation strategy can be proposed to increase the search range of the algorithm and motivate SCA-SMA to explore more promising regions.To evaluate the performance of the algorithm,SCA-SMA is experimentally validated in five different aspects.The results show that SCA-SMA is significantly competitive compared to advanced MAs.In particular,in facing the WSN node coverage problem,SCA-SMA has more obvious advantages in both average coverage and optimal coverage,which makes it possible to fully utilize the sensing range of each sensor node,while avoiding the waste of resources and the generation of monitoring blind zones.展开更多
The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned gr...The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned ground vehicle(UGV)within the energy-hungry WSN.This allows nodes,including sensors and the UGV,to share their energy using wireless power transfer techniques.To optimize the UGV's trajectory,we have developed a tabu searchbased method for global optimality,followed by a clustering-based method suitable for real-world applications.When the UGV reaches a stopping point,it functions as a regular sensor with ample battery.Accordingly,we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment.Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime.This effect is especially prominent in sparsely connected WSNs compared to highly connected ones,and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62461041Natural Science Foundation of Jiangxi Province under Grant No.20224BAB212016 and No.20242BA B25068China Scholarship Council under Grant No.202106825021.
文摘Energy efficiency is critical in Wireless Sensor Networks(WSNs)due to the limited power supply.While clustering algorithms are commonly used to extend network lifetime,most of them focus on single-layer optimization.To this end,an Energy-efficient Cross-layer Clustering approach based on the Gini(ECCG)index theory was proposed in this paper.Specifically,a novel mechanism of Gini Index theory-based energy-efficient Cluster Head Election(GICHE)is presented based on the Gini Index and the expected energy distribution to achieve balanced energy consumption among different clusters.In addition,to improve inter-cluster energy efficiency,a Queue synchronous Media Access Control(QMAC)protocol is proposed to reduce intra-cluster communication overhead.Finally,extensive simulations have been conducted to evaluate the effectiveness of ECCG.Simulation results show that ECCG achieves 50.6%longer the time until the First Node Dies(FND)rounds,up to 30%lower energy consumption compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),and higher throughput under different traffic loads,thereby validating its effectiveness in improving energy efficiency and prolonging the network lifetime.
基金supported by special project of the National Natural Science Foundation of China[No.42027806]special Fund of the National Natural Science Foundation of China[No.42041006]+3 种基金the National Key Research and Development Program Project of China[No.2018YFC1504705]the Key Program of the National Natural Science Foundation of China[No.61731015]the major instrument,the project of Natural Science Foundation in Shaanxi Province[No.2018JM6029]the Key Research and Development Program of Shaanxi[No.2022GY-331].
文摘The WSN(wireless sensor network)node optimization problem faces the challenge of efficient deployment and adaptation under limited resources and a dynamically changing environment.The complex and changing deployment environment puts higher requirements on the search space,computational cost,and optimization efficiency of the algorithms.For this reason,a slime mould algorithm called SCA-SMA is proposed to solve the above problem.In SCA-SMA,a reverse Sobol sequence is used to initialize the population to increase the population diversity and improve the probability of approaching the optimal solution.To better balance local exploitation and global exploration,a dynamic selection of sine cosine update mechanism is proposed:using an optimal position selection mechanism in the global exploration phase to avoid local optima,and integrating the sine cosine algorithm in the local exploitation phase to improve the mucilage position update method,enrich the optimization search process and enhance the development capability of the algorithm.Finally,an adaptive mutation strategy can be proposed to increase the search range of the algorithm and motivate SCA-SMA to explore more promising regions.To evaluate the performance of the algorithm,SCA-SMA is experimentally validated in five different aspects.The results show that SCA-SMA is significantly competitive compared to advanced MAs.In particular,in facing the WSN node coverage problem,SCA-SMA has more obvious advantages in both average coverage and optimal coverage,which makes it possible to fully utilize the sensing range of each sensor node,while avoiding the waste of resources and the generation of monitoring blind zones.
基金supported by the National Natural Science Foundation of China(No.62171486 and No.U2001213)the Guangdong Basic and Applied Basic Research Project(2022A1515140166)。
文摘The lifetime of a wireless sensor network(WSN)is crucial for determining the maximum duration for data collection in Internet of Things applications.To extend the WSN's lifetime,we propose deploying an unmanned ground vehicle(UGV)within the energy-hungry WSN.This allows nodes,including sensors and the UGV,to share their energy using wireless power transfer techniques.To optimize the UGV's trajectory,we have developed a tabu searchbased method for global optimality,followed by a clustering-based method suitable for real-world applications.When the UGV reaches a stopping point,it functions as a regular sensor with ample battery.Accordingly,we have designed optimal data and energy allocation algorithms for both centralized and distributed deployment.Simulation results demonstrate that the UGV and energy-sharing significantly extend the WSN's lifetime.This effect is especially prominent in sparsely connected WSNs compared to highly connected ones,and energy-sharing has a more pronounced impact on network lifetime extension than UGV mobility.
