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一种多约束目标选择算法在DTN网络中的运用研究

Research on Key Issues of Message Ferry-Based Routing in DTN
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摘要 DTN网络由于节点分布的稀疏性、节点移动的随机性、消息通信的不确定性使得网络拓扑频繁变化、通信链路经常中断、传输延迟相对较长,直接影响DTN网络的性能。Mes—sageFerry路由机制给DTN这种资源受限的网络场景中的数据通信提供了一个有效的手段。通过对MessageFerry目标节点的选择技术进行研究,提出了多约束条件下的目标选择算法——MCTSA。实验结果表明:MCTSA算法相对于原有的单一约束目标选择算法在消息传递率、传输延迟和网络负载等方面的性能都有了很大的提升。 Due to the sparse distribution of the DTN nodes,the randomness of node mobility and the uncertainty of communication, the network topology frequently changes, communica- tion links are often interrupted and the propagation delay is relatively long, which directly affect the DTN network performance. The Message-Ferry-based routing mechanism provides an effective means to improve the performance of DTN network. Concentrates on the target node selection technology of Ferry and proposes the Multi-Constraint Target Selection Algo- rithm (MCTSA). Experimental results show that the message delivery rate, propagation delay and the overhead ration of the DTN have been greatly improved using MCTSA compa- ring with the original single constraint target selection algorithm.
作者 胡伟
出处 《武警工程大学学报》 2016年第4期5-7,共3页 Journal of Engineering University of the Chinese People's Armed Police Force
关键词 消息摆渡 DTN路由 多约束目标选择算法 Message Ferry DTN route Multi-Constraint Target Selection Algorithm
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  • 1Kennedy J, Eberhart R C. Particle swarm optimization[ A]. Pro- ceedings of IEEE International Conference on Neural Networks [C]. NJ: 1EEE Piscataway, 1995. 1942 - 1948.
  • 2Coello Coello C A,Pulido G T, Lechuga M S. Handling mul- tiple objectives with particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation, 2004,8 (3) : 256 - 279.
  • 3Tsai S J, Sun T Y, et al. An improved multi-objective particle swarm optimizer for multi-objective problems[J]. Expert Sys- tems with Applications, 2010,37 (8) : 5872 - 5886.
  • 4Wang Y J, Yang Y P.Particle swarm optimization with prefer- ence order ranking for multi-objective optimization[J]. Infor- rnation Sciences, 2009,179 (12) : 1944 - 1959.
  • 5Sift Y H, Eberbart R C. A modified particle swarm optimizer [A]. Proceedings of the IF, RE International Conference on Evo- lutionary Computation[ C]. NJ: 1F, F,F, Piscataway, 1998.63 - 79.
  • 6Leong W F. Multiobjective Paricle Swarm Optimization: Inte- gration of Dynamic Population and Multiple-swarm Concepts and Constraint Handing [ D ]. Stillwater: Oklahoma State Uni- versity, 2008.
  • 7Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-[J]. 1EEE Transac- tions on Evolutionary Computatiorks, 2002,6 (2) : 182 - 197.
  • 8Coello Coello C A, Van Veldhuizen D A, "Larnont G B. Evo- lutionary Algorithrns for Solving Multi-objective Problems [M]. Norwell, MA: Kluwer, 2002.
  • 9彭志平,陈珂.一种消解协商僵局的多目标粒子群优化算法[J].电子学报,2007,35(8):1452-1457. 被引量:7
  • 10Pietzueh P, Muhl G, Fiege L. Distributed Event-Based Systems: An Emerging Community [J]. Distributed Systems Online, IEEE, 2007,8(2) : 1-3.

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