Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power facto...Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.展开更多
Recent years have witnessed the surge of asynchronous parallel(asyncparallel)iterative algorithms due to problems involving very large-scale data and a large number of decision variables.Because of asynchrony,the iter...Recent years have witnessed the surge of asynchronous parallel(asyncparallel)iterative algorithms due to problems involving very large-scale data and a large number of decision variables.Because of asynchrony,the iterates are computed with outdated information,and the age of the outdated information,which we call delay,is the number of times it has been updated since its creation.Almost all recent works prove convergence under the assumption of a finite maximum delay and set their stepsize parameters accordingly.However,the maximum delay is practically unknown.This paper presents convergence analysis of an async-parallel method from a probabilistic viewpoint,and it allows for large unbounded delays.An explicit formula of stepsize that guarantees convergence is given depending on delays’statistics.With p+1 identical processors,we empirically measured that delays closely follow the Poisson distribution with parameter p,matching our theoretical model,and thus,the stepsize can be set accordingly.Simulations on both convex and nonconvex optimization problems demonstrate the validness of our analysis and also show that the existing maximum-delay-induced stepsize is too conservative,often slows down the convergence of the algorithm.展开更多
基金supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:HI21C1831)the Soonchunhyang University Research Fund.
文摘Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.
基金This project was supported by the National Science Foundation(EAGER ECCS-1462397,DMS-1621798,and DMS-1719549).
文摘Recent years have witnessed the surge of asynchronous parallel(asyncparallel)iterative algorithms due to problems involving very large-scale data and a large number of decision variables.Because of asynchrony,the iterates are computed with outdated information,and the age of the outdated information,which we call delay,is the number of times it has been updated since its creation.Almost all recent works prove convergence under the assumption of a finite maximum delay and set their stepsize parameters accordingly.However,the maximum delay is practically unknown.This paper presents convergence analysis of an async-parallel method from a probabilistic viewpoint,and it allows for large unbounded delays.An explicit formula of stepsize that guarantees convergence is given depending on delays’statistics.With p+1 identical processors,we empirically measured that delays closely follow the Poisson distribution with parameter p,matching our theoretical model,and thus,the stepsize can be set accordingly.Simulations on both convex and nonconvex optimization problems demonstrate the validness of our analysis and also show that the existing maximum-delay-induced stepsize is too conservative,often slows down the convergence of the algorithm.
