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Hierarchical distributed management clustering protocol for wireless sensor networks

Author

Listed:
  • Amin Shahraki

    (Østfold University College)

  • Marjan Kuchaki Rafsanjani

    (Shahid Bahonar University of Kerman)

  • Arsham Borumand Saeid

    (Shahid Bahonar University of Kerman)

Abstract

In recent years, there has been a marked increase in the use of wireless sensor networks in various environments such as crisis areas, military operations, and monitoring systems. These networks do not use a fixed network infrastructure and therefore they are a popular choice for highly dynamic environments. One of the main concerns in these networks is the topology management issue, which the clustering method is a subfield for that. The main objective of clustering methods is optimizing the energy consumption. This paper proposes a new clustering protocol, which uses many parameters such as the activity history of each node, local and general state of nodes and their resources condition to determine the best cluster heads and members of each cluster that can increase the network lifetime, fair resource consumption and network coverage.

Suggested Citation

  • Amin Shahraki & Marjan Kuchaki Rafsanjani & Arsham Borumand Saeid, 2017. "Hierarchical distributed management clustering protocol for wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 65(1), pages 193-214, May.
    • Handle: RePEc:spr:telsys:v:65:y:2017:i:1:d:10.1007_s11235-016-0218-7
    DOI: 10.1007/s11235-016-0218-7
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    Citations

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    Cited by:

    1. Muhammad Ilyas & Zahid Ullah & Fakhri Alam Khan & Muhammad Hasanain Chaudary & Muhammad Sheraz Arshed Malik & Zafar Zaheer & Hamood Ur Rehman Durrani, 2020. "Trust-based energy-efficient routing protocol for Internet of things–based sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(10), pages 15501477209, October.
    2. Saneh Lata Yadav & R. L. Ujjwal, 2021. "Mitigating congestion in wireless sensor networks through clustering and queue assistance: a survey," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2083-2098, December.

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