摘要
Epidemic算法在某些场景中具有很高的传输成功率、很小的传输延迟,但算法的适应性较差,在另一些场景中算法性能会显著下降。理论和实验分析表明,挤出效应是导致Epidemic算法性能下降的主要原因。分析了具有免疫机制Epidemic算法的性能,指出了该机制的缺陷,提出了退避机制:当某一节点缓冲区饱和时,不再接收与之相遇节点的数据包。在ONE仿真平台上实现了具有退避机制的Ep-idemic算法,实验结果表明,在挤出效应显著的场景下,退避机制能有效地抑制挤出效应,改进后算法的传输成功率有大幅度的提高,路由开销也有一定程度的下降。
In some scenarios, Epidemic algorithm has high delivery ratio, small delivery delay, but poor adaptability＆ Moreover, the performance of the algorithm will significantly degrade in other sce- narios. On the basis of analysis of the factors affecting the algorithm performance, Crowding-（）ut effect is considered as the main reason leading to negative performance. In this paper, the performance of Epi- demic algorithm with immune mechanism is analyzed and some defects of the immune mechanism are in- dicated. Therefore, an improved algorithm is formulated with a kind of Back-off mechanism, so that the node will no longer receive packets from meeting nodes when its buffer is close to saturation. The prom- ising results on the ONE simulation platform show that the proposed algorithm can effectively suppress Crowding-Out effect and greatly improve the delivery ratio and reduce the routing overhead to some ex- tend under various scenarios.
出处
《计算机工程与科学》
CSCD
北大核心
2013年第3期66-71,共6页
Computer Engineering & Science
基金
国家自然科学基金资助项目(61170112)
北京市属高等学校科学技术与研究生教育创新工程建设项目(PXM2012_014213_000079)
关键词
机会网络
路由算法
EPIDEMIC
挤出效应
退避机制
opportunistic network
routing algorithm
epidemic
crowding-out effect
backoff mecha-nism
