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

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

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 3, 1550147719839581

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.

Keywords: Wireless sensor networks; distributed data fusion; neural network; mobile sink; energy efficiency (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:3:p:1550147719839581

DOI: 10.1177/1550147719839581

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