Title: A data validity and energy efficiency sensitive method for in-node multi-parameter collaborative sensing in internet of things for agriculture

Authors: Zhaokang Gong; Xiaomin Li; Rihong Zhang; Yongxin Liu

Addresses: School of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China ' School of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China ' School of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China ' School the Department of Mathematics, Embry-Riddle Aeronautical University, Daytona Beach, FL 32117, USA

Abstract: In the agricultural IoT, there are problems such as low data value and high energy consumption. To this end, a cloud-edge intelligence-supported intra-node multi-parameter collaborative sensing strategy that is sensitive to data validity and energy efficiency is proposed. Firstly, a node intra-node data sensing framework supported by cloud-edge intelligence is proposed, a mathematical model of multi-parameter data sensing tasks is established, and evaluation indicators such as energy efficiency are designed. Secondly, an in-node multi-parameter data sensing based on correlation is proposed. The correlation between tasks and parameters is analysed using grey correlation to quantify the data value and validity of parameters. Using edge intelligence technology, the intra-node sensors are optimised with high value data as the target. Simulation experiments show that this method is superior to traditional methods in data value density, data validity rate and energy efficiency, with improvements of 100%, 20.59% and 300% respectively.

Keywords: multi-parameter collaborative sensing; data validity; energy efficiency; internet of things; IoT.

DOI: 10.1504/IJSNET.2025.147631

International Journal of Sensor Networks, 2025 Vol.48 No.3, pp.135 - 148

Received: 11 Aug 2024
Accepted: 29 Mar 2025
Published online: 24 Jul 2025 *

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