计算机科学与探索 ›› 2019, Vol. 13 ›› Issue (4): 620-628.DOI: 10.3778/j.issn.1673-9418.1712058

• 网络与信息安全 • 上一篇    下一篇

V-NDN中热点内容推送策略研究

史锦山,李  茹+,李瑛琦   

  1. 内蒙古大学 计算机学院,呼和浩特 010021
  • 出版日期:2019-04-01 发布日期:2019-04-10

Research on Hot Content Push Strategy in V-NDN

SHI Jinshan, LI Ru+, LI Yingqi   

  1. College of Computer Science, Inner Mongolia University, Hohhot 010021, China
  • Online:2019-04-01 Published:2019-04-10

摘要:

在V-NDN(vehicular named data networking)中,因为车辆的移动性而导致未响应兴趣包的概率大大增加。目前的解决方法是车辆节点缓存所有收听到的数据包,但这种方法会使节点中缓存大量重复的数据包副本,增加缓存的开销。为了解决此问题,提出了一种适用于城市道路的热点内容推送算法。首先,提出了一种热点内容挖掘算法,将V-NDN中可能的热点内容从大量的数据中挖掘出来;然后,通过热点内容推送算法将热点内容推送给其他可能访问这些内容的节点,以此提高网络性能;最后,从理论上分析了热点内容挖掘时需要考虑的影响因素。仿真结果表明,与贪婪转发策略相比,添加了热点内容推送算法会使请求满足率提高4.6%到14.1%,缓存命中率增加了16.6%到33.0%。

关键词: 命名数据网络(NDN), 车辆自组织网络(VANET), 热点内容挖掘, 热点内容推送, 转发策略优化

Abstract: In vehicular named data networking (V-NDN), the probability of unresponsive interest packets increases greatly because of the mobility of the vehicles. The current solution is that the vehicle node caches all the received packets, but this approach makes nodes cache a large amount of duplicate replica packets and increases the overhead of caching. In order to solve this problem, a hot content push algorithm for urban road is proposed. First of all, this paper proposes a hot topic mining algorithm used to mine the hot content from massive amounts of data. Then, through the hot content push algorithm, the hot topics will be pushed to other nodes which may access these contents to improve the network performance, in order to improve the network performance. Finally, the influencing factors should be considered when mining hot contents theoretically. Simulation results indicate that, compared with the greedy forwarding strategy, the addition of hot content push algorithm increases the request satisfaction rate by 4.6% to 14.1%, and increases the cache hit rate by 16.6% to 33.0%.

Key words: named data networking (NDN), vehicular ad-hoc networks (VANET), hot content mining, hot content push, forwarding strategy optimization