Network management framework and lifetime evaluation method for wireless sensor networks

Jaewoo Kim, Hahnearl Jeon, Jaiyong Lee

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Network Management is the process of managing, monitoring, and controlling the network. Conventional network management was based on wired network which is heavy and unsuitable for resource constrained Wireless Sensor Networks (WSNs). WSNs can be large scale networks and it is impossible to manage each node individually. Also, the polling mechanism of Simple Network Management Protocol (SNMP) imposes heavy management traffic overhead. Since management messages consume resources of WSNs, it can affect the performance of the network. Therefore, it is necessary for WSNs to perform energy efficient network management. Moreover, the management of WSNs should support sustainability. In this paper, we propose a network management framework for WSNs to reduce management traffic. Based on the cluster-based network management architecture, we classify the Management Information Base (MIB) and define the management messages and message exchange operation for each kind of MIB. The analysis result indicates that the proposed framework can reduce management traffic compared to the polling mechanism and there exists an optimal number of clusters for a given number of nodes. We also propose a lifetime estimation method as an application of the proposed framework. The proposed method uses messages of the proposed framework and estimate the network lifetime from the received management information. Simulation result shows that the proposed method can evaluate the network lifetime and reduce the number exchanged message by using the cluster-based architecture.

Original languageEnglish
Pages (from-to)165-178
Number of pages14
JournalIntegrated Computer-Aided Engineering
Issue number2
Publication statusPublished - 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Artificial Intelligence


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