Self-Organized Data Aggregation among Selfish Nodes in an Isolated Cluster

K. Habibul Kabir Masahiro Sasabe Tetsuya Takine

In Proc. of International Conference of Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS), 2010

Abstract

This paper considers a delay tolerant network, where a message ferry travels multiple isolated clusters, collects data from nodes in the clusters, and finally delivers the data to a sink node. In our previous work, we proposed a self-organized data aggregation technique for collecting data from nodes efficiently, which can automatically accumulate data from cluster members to a limited number of cluster members called aggregators. The proposed scheme was developed based on the evolutionary game theoretic approach, in order to take account of the inherent selfishness of the nodes for saving their own battery life. The number of aggregators can be controlled to a desired value by adjusting the energy that the message ferry supplies to the aggregators. In this paper, we further examine the proposed system in terms of success of data transmission and system survivability. We first introduce a new type of game model with retransmissions. Through both theoretic and simulation approaches, we then reveal feasible parameter settings which can achieve a system with desirable characteristics: Stability, survival, and successful data transfer.

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Text Reference

K. Habibul Kabir, Masahiro Sasabe, Tetsuya Takine, Self-Organized Data Aggregation among Selfish Nodes in an Isolated Cluster, Proc. of International Conference of Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS), pp.264-275, December 2010.

BibTex Reference

@inproceedings{kabir10SelfOrganizedDataAggregation,
    author = "Kabir, K. Habibul and Sasabe, Masahiro and Takine, Tetsuya",
    editor = "Suzuki, Junichi and Nakano, Tadashi",
    title = "Self-{{Organized Data Aggregation}} among {{Selfish Nodes}} in an {{Isolated Cluster}}",
    booktitle = "Proc. of {{International Conference}} of {{Bio-Inspired Models}} of {{Network}}, {{Information}}, and {{Computing Systems}} ({{BIONETICS}})",
    year = "2010",
    month = "December",
    pages = "264--275",
    doi = "10.1007/978-3-642-32615-8\_27"
}