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International Journal of Crowd Science 2022, 6(1): 34-43 https://doi.org/10.26599/IJCS.2022.9100007
Open Access | Issue | Published: 15 April 2022

Energy-Efficient Sensory Data Collection Based on Spatiotemporal Correlation in IoT Networks

Show Author's Information Jine Tang1,  Shuang Wu2,  Lingxiao Wei1,  Weijing Liu3( ),  Taishan Qin1,  Zhangbing Zhou2,  Junhua Gu1
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Tianjin Institute of Aerospace Mechanical and Electrical Equipment and Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology, Tianjin 300301, China
Keywords:
Internet of Things, data collection, energy-saving, spatiotemporal correlation
Cite this article:
Tang J, Wu S, Wei L, et al. Energy-Efficient Sensory Data Collection Based on Spatiotemporal Correlation in IoT Networks. International Journal of Crowd Science, 2022, 6(1): 34-43. https://doi.org/10.26599/IJCS.2022.9100007
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Abstract Full text About this article

The Internet of Things (IoT) is currently in a stage of rapid development. Hundreds of millions of sensing nodes and intelligent terminals undertake the tasks of sensing and transmitting data. Data collection is the key to realizing data analysis and intelligent application of IoT. The life cycle of IoT is limited by the energy of the IoT nodes in the network. A complex computing model will bring serious or even unbearable burdens to IoT nodes. In this study, we use the data prediction method to explore time correlation data and adjust the appropriate spatial sampling rate on the basis of the spatial correlation of sensory data to further reduce data. Specifically, the improved and optimized DNA-binding protein (DBP) data prediction method can increase the time interval of sensing data to further reduce energy consumption. Based on the spatial characteristics of the sensing data, substituting the data of similar nodes can reduce the sampling rate. The probabilistic wake-up strategy is also adopted to adjust the spatial correlation of the sensing data. On the basis of node priority, an optimized greedy algorithm is proposed to select the appropriate dominating node for eliminating redundant nodes and improving network energy utilization. Experiments have proven that our scheme reduces network energy consumption under the premise of ensuring data reliability.


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Energy-Efficient Sensory Data Collection Based on Spatiotemporal Correlation in IoT Networks

Show Author's information Jine Tang1, Shuang Wu2, Lingxiao Wei1, Weijing Liu3( ), Taishan Qin1, Zhangbing Zhou2, Junhua Gu1
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
School of Information Engineering, China University of Geosciences, Beijing 100083, China
Tianjin Institute of Aerospace Mechanical and Electrical Equipment and Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology, Tianjin 300301, China

Abstract

The Internet of Things (IoT) is currently in a stage of rapid development. Hundreds of millions of sensing nodes and intelligent terminals undertake the tasks of sensing and transmitting data. Data collection is the key to realizing data analysis and intelligent application of IoT. The life cycle of IoT is limited by the energy of the IoT nodes in the network. A complex computing model will bring serious or even unbearable burdens to IoT nodes. In this study, we use the data prediction method to explore time correlation data and adjust the appropriate spatial sampling rate on the basis of the spatial correlation of sensory data to further reduce data. Specifically, the improved and optimized DNA-binding protein (DBP) data prediction method can increase the time interval of sensing data to further reduce energy consumption. Based on the spatial characteristics of the sensing data, substituting the data of similar nodes can reduce the sampling rate. The probabilistic wake-up strategy is also adopted to adjust the spatial correlation of the sensing data. On the basis of node priority, an optimized greedy algorithm is proposed to select the appropriate dominating node for eliminating redundant nodes and improving network energy utilization. Experiments have proven that our scheme reduces network energy consumption under the premise of ensuring data reliability.

Keywords: Internet of Things, data collection, energy-saving, spatiotemporal correlation

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Received: 03 January 2022
Revised: 05 March 2022
Accepted: 05 March 2022
Published: 15 April 2022
Issue date: April 2022

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© The author(s) 2022

Acknowledgements

Acknowledgment

This work was supported in part by the Open Project Program of Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology, Tianjin Institute of Aerospace Mechanical and Electrical Equipment (NO. ZNZB-2021-01) and in part by the National Natural Science Foundation of China (NO. 61702232).

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