Management of moving network clusters

Master's thesis

Jari Tenhunen

Research output: ThesisMaster's thesisTheses

Abstract

This work presents a clustering protocol that is able identify and manage moving network clusters. The clusters are detected based on the nodes’ proximity with an associativity-based mechanism. After the cluster is set up, individual nodes can join and leave the cluster, and two clusters can be merged as well. The protocol also features a mechanism for reducing the forwarding of broadcast control messages. The protocol’s functionality and performance was evaluated using simulations. In addition to the basic functionality, the simulation scenarios tested the overhead of the protocol’s control traffic and its effect on the application data flow. The results show that the clustering protocol achieves its goals and is able to identify clusters and perform the basic management operations. It was discovered that the associativity-based neighbour discovery mechanism induces only a little overhead, but the real challenge is in choosing the right parameters, which in turn depend on the use environment and radio technology characteristics. Based on the results, it can be concluded that the presented protocol is well suited for its tasks, and it also looks promising for future work. It is advisable to continue research especially on scalability, routing optimisations, and enhancing the neighbour discovery method.
Original languageEnglish
QualificationMaster Degree
Awarding Institution
  • University of Oulu
Place of PublicationOulu
Publisher
Publication statusPublished - 2005
MoE publication typeG2 Master's thesis, polytechnic Master's thesis

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Traffic control
Scalability

Keywords

  • Clustering protocol
  • heterogeneous ad hoc networks
  • personal area networks

Cite this

Tenhunen, J. (2005). Management of moving network clusters: Master's thesis. Oulu: University of Oulu.
Tenhunen, Jari. / Management of moving network clusters : Master's thesis. Oulu : University of Oulu, 2005. 69 p.
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abstract = "This work presents a clustering protocol that is able identify and manage moving network clusters. The clusters are detected based on the nodes’ proximity with an associativity-based mechanism. After the cluster is set up, individual nodes can join and leave the cluster, and two clusters can be merged as well. The protocol also features a mechanism for reducing the forwarding of broadcast control messages. The protocol’s functionality and performance was evaluated using simulations. In addition to the basic functionality, the simulation scenarios tested the overhead of the protocol’s control traffic and its effect on the application data flow. The results show that the clustering protocol achieves its goals and is able to identify clusters and perform the basic management operations. It was discovered that the associativity-based neighbour discovery mechanism induces only a little overhead, but the real challenge is in choosing the right parameters, which in turn depend on the use environment and radio technology characteristics. Based on the results, it can be concluded that the presented protocol is well suited for its tasks, and it also looks promising for future work. It is advisable to continue research especially on scalability, routing optimisations, and enhancing the neighbour discovery method.",
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year = "2005",
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Tenhunen, J 2005, 'Management of moving network clusters: Master's thesis', Master Degree, University of Oulu, Oulu.

Management of moving network clusters : Master's thesis. / Tenhunen, Jari.

Oulu : University of Oulu, 2005. 69 p.

Research output: ThesisMaster's thesisTheses

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N2 - This work presents a clustering protocol that is able identify and manage moving network clusters. The clusters are detected based on the nodes’ proximity with an associativity-based mechanism. After the cluster is set up, individual nodes can join and leave the cluster, and two clusters can be merged as well. The protocol also features a mechanism for reducing the forwarding of broadcast control messages. The protocol’s functionality and performance was evaluated using simulations. In addition to the basic functionality, the simulation scenarios tested the overhead of the protocol’s control traffic and its effect on the application data flow. The results show that the clustering protocol achieves its goals and is able to identify clusters and perform the basic management operations. It was discovered that the associativity-based neighbour discovery mechanism induces only a little overhead, but the real challenge is in choosing the right parameters, which in turn depend on the use environment and radio technology characteristics. Based on the results, it can be concluded that the presented protocol is well suited for its tasks, and it also looks promising for future work. It is advisable to continue research especially on scalability, routing optimisations, and enhancing the neighbour discovery method.

AB - This work presents a clustering protocol that is able identify and manage moving network clusters. The clusters are detected based on the nodes’ proximity with an associativity-based mechanism. After the cluster is set up, individual nodes can join and leave the cluster, and two clusters can be merged as well. The protocol also features a mechanism for reducing the forwarding of broadcast control messages. The protocol’s functionality and performance was evaluated using simulations. In addition to the basic functionality, the simulation scenarios tested the overhead of the protocol’s control traffic and its effect on the application data flow. The results show that the clustering protocol achieves its goals and is able to identify clusters and perform the basic management operations. It was discovered that the associativity-based neighbour discovery mechanism induces only a little overhead, but the real challenge is in choosing the right parameters, which in turn depend on the use environment and radio technology characteristics. Based on the results, it can be concluded that the presented protocol is well suited for its tasks, and it also looks promising for future work. It is advisable to continue research especially on scalability, routing optimisations, and enhancing the neighbour discovery method.

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Tenhunen J. Management of moving network clusters: Master's thesis. Oulu: University of Oulu, 2005. 69 p.