Distributed multi-representative re-fusion approach for heterogeneous sensing data collection

Anfeng Liu; Xiao Liu; Tianyi Wei; Laurence T. Yang; Seungmin Rho; Anand Paul

doi:10.1145/2974021

Distributed multi-representative re-fusion approach for heterogeneous sensing data collection

Anfeng Liu, Xiao Liu, Tianyi Wei, Laurence T. Yang, Seungmin Rho, Anand Paul

Research output: Contribution to journal › Article › peer-review

29 Scopus citations

Abstract

Amulti-representative re-fusion (MRRF) approximate data collection approach is proposed in whichmultiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.

Original language	English
Article number	73
Journal	Transactions on Embedded Computing Systems
Volume	16
Issue number	3
DOIs	https://doi.org/10.1145/2974021
State	Published - May 2017

Keywords

Approximate data collection
Multirepresentative re-fusion
Network lifetime
Wireless sensor networks

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1145/2974021

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abstract = "Amulti-representative re-fusion (MRRF) approximate data collection approach is proposed in whichmultiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.",

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author = "Anfeng Liu and Xiao Liu and Tianyi Wei and Yang, {Laurence T.} and Seungmin Rho and Anand Paul",

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AU - Liu, Xiao

AU - Wei, Tianyi

AU - Yang, Laurence T.

AU - Rho, Seungmin

AU - Paul, Anand

PY - 2017/5

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N2 - Amulti-representative re-fusion (MRRF) approximate data collection approach is proposed in whichmultiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.

AB - Amulti-representative re-fusion (MRRF) approximate data collection approach is proposed in whichmultiple nodes with similar readings form a data coverage set (DCS). The reading value of the DCS is represented by an R-node. The set near the Sink is smaller, while the set far from the Sink is larger, which can reduce the energy consumption in hotspot areas. Then, a distributed data-aggregation strategy is proposed that can re-fuse the value of R-nodes that are far from each other but have similar readings. Both comprehensive theoretical and experimental results indicate that the MRRF approach increases lifetime and energy efficiency.

KW - Approximate data collection

KW - Multirepresentative re-fusion

KW - Network lifetime

KW - Wireless sensor networks

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Distributed multi-representative re-fusion approach for heterogeneous sensing data collection

Abstract

Keywords

UN SDGs

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