The TTL Prediction Model of Probability Routing Algorithm with the Transmission Rate Guaranteed

MA Xue-bin; WANG Ying-biao; GAO Rui-chao; ZHANG Xiao-juan

doi:10.3969/j.issn.0372-2112.2018.11.015

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The TTL Prediction Model of Probability Routing Algorithm with the Transmission Rate Guaranteed

更新时间：2025-07-16

- The TTL Prediction Model of Probability Routing Algorithm with the Transmission Rate Guaranteed
- Acta Electronica Sinica Vol. 46, Issue 11, Pages: 2679-2687(2018)
- 作者机构：
  
  内蒙古大学计算机学院, 内蒙古自治区呼和浩特,010021
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2018.11.015
  CLC： TN92
- Published Online：25 November 2018，
  
  Published：2018
- 稿件说明：
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MA Xue-bin, WANG Ying-biao, GAO Rui-chao, et al. The TTL Prediction Model of Probability Routing Algorithm with the Transmission Rate Guaranteed[J]. Acta Electronica Sinica, 2018, 46(11): 2679-2687.
DOI：

MA Xue-bin, WANG Ying-biao, GAO Rui-chao, et al. The TTL Prediction Model of Probability Routing Algorithm with the Transmission Rate Guaranteed[J]. Acta Electronica Sinica, 2018, 46(11): 2679-2687. DOI： 10.3969/j.issn.0372-2112.2018.11.015.

摘要

概率路由算法是机会网络中一种常用的路由算法，其TTL的设置将直接影响路由的性能.本文首先利用马尔可夫链对概率路由的转发过程进行建模，并从消息的传输成功率和传输延时方面进行性能评估，不仅可以计算出一个消息从源节点到目的节点所需的传输延时，还可以预测出在给定传输成功率下所需要的最短消息生存周期TTL，这对于TTL的设置具有理论上的指导作用.其次，本文建立的预测模型还可以使一些无法到达目的节点的消息及时被删除，从而减少不必要的转发，达到节约网络资源的目的.最后，在两个真实数据集中的实验结果表明，本文提出的模型能够为概率路由中TTL值的准确设置提供有效参考.

Abstract

Probabilistic routing algorithm is a common routing algorithm in opportunistic networks

and its TTL will directly affect the routing performance. Firstly

in the paper we uses Markov chain model to evaluate the transmission rate and the transmission delay of probabilistic routing. It can not only calculate the transmission delay for a message from a source node to a destination node

but also can predict the shortest TTL in a given transmission rate

which can provide theoretical guidance for the setting of TTL. Secondly

this prediction model can delete the messages which cannot be relayed to destination nodes to reduce unnecessary forwarding. Therefore

The model can be used to save the network resources maximally. Finally

we experimentally evaluate our model on two real data set

and demonstrate that it can provide a powerful reference for the setting of TTL values accurately in probabilistic routing.

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