视频点播(VOD)是以用户需求为主导的视频系统,如何提高视频点播(VOD)系统的可扩展性和在动态环境中的可靠性,成为视频点播系统大规模应用的关键。文中提出了一种新的基于P2P(Peer to peer)的VOD(Video-on-demand)系统,并阐述了系统设计...视频点播(VOD)是以用户需求为主导的视频系统,如何提高视频点播(VOD)系统的可扩展性和在动态环境中的可靠性,成为视频点播系统大规模应用的关键。文中提出了一种新的基于P2P(Peer to peer)的VOD(Video-on-demand)系统,并阐述了系统设计所采用的相关技术与方法。系统中考虑了节点均衡负载对系统整体性能的影响,采用服务器集中调度与节点分布协调管理相结合的资源定位方式,灵活的候选父节点策略使节点失效后能进行快速的失效恢复,提高了VOD系统的可扩展性和动态环境中的可靠性。展开更多
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
文摘视频点播(VOD)是以用户需求为主导的视频系统,如何提高视频点播(VOD)系统的可扩展性和在动态环境中的可靠性,成为视频点播系统大规模应用的关键。文中提出了一种新的基于P2P(Peer to peer)的VOD(Video-on-demand)系统,并阐述了系统设计所采用的相关技术与方法。系统中考虑了节点均衡负载对系统整体性能的影响,采用服务器集中调度与节点分布协调管理相结合的资源定位方式,灵活的候选父节点策略使节点失效后能进行快速的失效恢复,提高了VOD系统的可扩展性和动态环境中的可靠性。
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.
