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Mobile Sink Data Gathering through Clustering (클러스터링을 통한 모바일 싱크 데이터 수집)

  • Park, Jang-Su;Ahn, Byoung-Chul
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.46 no.5
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    • pp.79-85
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    • 2009
  • A sink node and its neighbor nodes spend more energy than other nodes since a stationary sink node collects data from wireless sensor networks(WSNs). For larger WSNs, the unbalanced energy of nodes causes the operation of WSNs to stop rapidly. This paper proposes a data gathering method by adapting the mobile sink to prolong the life time of large WSNs. After partitioning a network into several clusters, a mobile sink visits each cluster and collects data from it. An efficient algorithm is proposed to improve the energy efficiency by delivering the message from the mobile sink to the cluster head as well as to reduce the data gathering delay, which is the disadvantage of the mobile sink. Also, The algorithm is analyzed for the energy consumption and the data gathering delay. The validity of the ananlysis result is confirmed by the simulation.

A Data Gathering Protocol for Multihop Transmission for Large Sensor Networks (대형 센서네트워크에서 멀티홉 전송을 이용한 데이터 수집 프로토콜)

  • Park, Jang-Su;Ahn, Byoung-Chul
    • Journal of KIISE:Information Networking
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    • v.37 no.1
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    • pp.50-56
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    • 2010
  • This paper proposes a data gathering method by adapting the mobile sink to prolong the whole operation time of large WSNs. After partitioning a network into several clusters, a mobile sink visits each cluster and collects data from it. An efficient protocol improves the energy efficiency by delivering messages from the mobile sink to the cluster head as well as reduces the data gathering delay, which is the disadvantage of the mobile sink. For the scalability of sensor network, the network architecture should support the multihop transmission in the duster rather than the single hop transmission. The process for the data aggregation linked to the travelling path is proposed to improve the energy consumption of intermediate nodes. The experiment results show that the proposed model is more efficient than legacy methods in the energy consumption and the data gathering time.