A stochastic performance estimation method for wireless multi-hop network

Shizuna Mori, Takahiro Yakoh

Research output: Contribution to journalArticlepeer-review


M2M (machine-to-machine) is required and spreads rapidly in recent years. Wireless multi-hop communication is one of the communication styles to construct M2M. However, the performance estimation of such wireless multi-hop communication is not so easy because the bit error ratio and symbol rate adaptation heavily depend on various environmental conditions. Stochastic network calculus (SNC) is a theory for the performance estimation of network. However, current SNC lacks ways to take wireless random errors, branching and joining of traffic flows into calculation, these are necessary to estimate the performance of sink-tree wireless-sensor network. This paper proposed a performance estimation method for sink-tree wireless multi-hop network based on SNC. The proposed method has three attractive points. Firstly, this introduces parameters of hop number and node number considering wireless random error due to packet collision. Secondly, this treats actual sink-tree network model by considering joining of traffic. Thirdly, this separates the service curve and bounding function calculation to avoid double counting. To evaluate the accuracy of the result of the proposed method, conventional estimation methods and simulation evaluation were conducted on a typical wireless multi-hop sensor data collection network. The results validate that the proposed method achieves much higher-accuracy than conventional methods.

Original languageEnglish
Pages (from-to)701-708
Number of pages8
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number5
Publication statusPublished - 2017


  • Performance estimation method
  • Stochastic network calculus
  • Wireless multi-hop network

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


Dive into the research topics of 'A stochastic performance estimation method for wireless multi-hop network'. Together they form a unique fingerprint.

Cite this