Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (5): 1397-1402.DOI: 10.11772/j.issn.1001-9081.2019081495

• Network and communications • Previous Articles     Next Articles

Opportunistic network message forwarding algorithm based on time-effectiveness of encounter probability and repeated diffusion perception

GE Yu1, LIANG Jing2   

  1. 1.College of Computer Science, Sichuan Normal University, ChengduSichuan 610068, China
    2.School of Computer Engineering, Chengdu Technological University, ChengduSichuan 610031, China
  • Received:2019-09-03 Revised:2019-12-07 Online:2020-05-10 Published:2020-05-15
  • Contact: LIANG Jing, born in 1979, M. S., lecturer. Her research interests machine learning.
  • About author:GE Yu, born in 1981, M. S., associate professor. His research interests include opportunistic network, computational intelligence.LIANG Jing, born in 1979, M. S., lecturer. Her research interests machine learning.
  • Supported by:

    This work is partially supported by the Foundation of Sichuan Education Department (16ZB0060).


葛宇1, 梁静2   

  1. 1.四川师范大学 计算机科学学院,成都 610068
    2.成都工业学院 计算机工程学院,成都 610031
  • 通讯作者: 梁静(1979-)
  • 作者简介:葛宇(1981—),男,重庆人,副教授,硕士,CCF会员,主要研究方向:机会网络、计算智能; 梁静(1979-),女,四川宜宾人,讲师,硕士,主要研究方向:机器学习。
  • 基金资助:



In order to select more reasonable relay nodes for message transmission and improve the efficiency of message delivery in opportunistic networks, message forwarding utility was designed, a corresponding message copy forwarding algorithm was proposed. Firstly, based on the historical encounter information of nodes, the indirect encounter probability of nodes and the corresponding time-effectiveness were analyzed, then a time-effectiveness indicator was proposed to evaluate the encounter information value. Secondly, combined with the similarity of node motion, the problem of message repeated diffusion was analyzed, a deviation indicator of node movement was proposed to evaluate the possibility of message repeated diffusion. Simulation results show that compared with Epidemic, ProPHET, Maxprop, SAW (Spray And Wait) algorithms, the proposed algorithm has better performance in delivery success rate, overhead and delay.

Key words: opportunistic network, relay node, delivery efficiency, time-effectiveness of encounter probability, node motion similarity


在机会网络中,为了更合理地选择消息传输中继节点并提高消息投递效率,设计了消息转发效用,并给出了对应的消息副本转发算法。首先,从节点历史相遇信息入手,重点分析了节点的间接相遇概率及其对应的时效性问题,提出了评估相遇信息价值的时效指标;然后,结合节点运动相似性分析了消息重复扩散问题,并提出节点移动偏离指标,用于评价节点重复扩散消息的可能性。仿真实验结果表明:与Epidemic、ProPHET、Maxprop和SAW(Spray And Wait)算法相比,综合考虑投递成功率、开销和延时指标,所提算法表现出了更好性能。

关键词: 机会网络, 中继节点, 投递效率, 相遇概率时效性, 节点运动相似性

CLC Number: