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An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks

Author

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  • Jin Wang
  • Yu Gao
  • Wei Liu
  • Arun Kumar Sangaiah
  • Hye-Jin Kim

Abstract

Numerous tiny sensors are restricted with energy for the wireless sensor networks since most of them are deployed in harsh environments, and thus it is impossible for battery re-change. Therefore, energy efficiency becomes a significant requirement for routing protocol design. Recent research introduces data fusion to conserve energy; however, many of them do not present a concrete scheme for the fusion process. Emerging machine learning technology provides a novel direction for data fusion and makes it more available and intelligent. In this article, we present an intelligent data gathering schema with data fusion called IDGS-DF. In IDGS-DF, we adopt a neural network to conduct data fusion to improve network performance. First, we partition the whole sensor fields into several subdomains by virtual grids. Then cluster heads are selected according to the score of nodes and data fusion is conducted in CHs using a pretrained neural network. Finally, a mobile agent is adopted to gather information along a predefined path. Plenty of experiments are conducted to demonstrate that our schema can efficiently conserve energy and enhance the lifetime of the network.

Suggested Citation

  • Jin Wang & Yu Gao & Wei Liu & Arun Kumar Sangaiah & Hye-Jin Kim, 2019. "An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 15(3), pages 15501477198, March.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:3:p:1550147719839581
    DOI: 10.1177/1550147719839581
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    References listed on IDEAS

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    1. Liu Yang & Yin-Zhi Lu & Yuan-Chang Zhong & Simon X. Yang, 2018. "An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(1), pages 11-26, May.
    2. Erfan Babaee Tirkolaee & Ali Asghar Rahmani Hosseinabadi & Mehdi Soltani & Arun Kumar Sangaiah & Jin Wang, 2018. "A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
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    5. Zi-Xuan Yu & Meng-Shi Li & Yi-Peng Xu & Sheraz Aslam & Yuan-Kang Li, 2021. "Techno-Economic Planning and Operation of the Microgrid Considering Real-Time Pricing Demand Response Program," Energies, MDPI, vol. 14(15), pages 1-28, July.
    6. Chang Zhou & Zhenghong Gu & Yu Gao & Jin Wang, 2019. "An Improved Style Transfer Algorithm Using Feedforward Neural Network for Real-Time Image Conversion," Sustainability, MDPI, vol. 11(20), pages 1-15, October.
    7. Ali Toolabi Moghadam & Bahram Bahramian & Farid Shahbaazy & Ali Paeizi & Tomonobu Senjyu, 2023. "Stochastic Flexible Power System Expansion Planning, Based on the Demand Response Considering Consumption and Generation Uncertainties," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    8. Fan Chao & Zhiqin He & Aiping Pang & Hongbo Zhou & Junjie Ge, 2019. "Path Optimization of Mobile Sink Node in Wireless Sensor Network Water Monitoring System," Complexity, Hindawi, vol. 2019, pages 1-10, November.

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