ArticleSensors (Basel, Switzerland)2020
A Data-driven Adaptive Sampling Method Based on Edge Computing.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Smart Energy Management in Agricultural Wireless Sensor Nodes Using TinyML-Based Adaptive Sampling.Sensors (Basel, Switzerland) · 2026Article
- Frugal Self-Optimization Mechanisms for Edge-Cloud Continuum.Sensors (Basel, Switzerland) · 2025Article
- Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2024Review
- An Adaptive Sampling Framework for Life Cycle Degradation Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Enabling Fine Sample Rate Settings in DSOs with Time-Interleaved ADCs.Sensors (Basel, Switzerland) · 2021Article
- Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
The rise of edge computing has promoted the development of the industrial internet of things (IIoT). Supported by edge computing technology, data acquisition can also support more complex and perfect application requirements in industrial field. Most of traditional sampling methods use constant sampling frequency and ignore the impact of changes of sampling objects during the data acquisition. For the problem of sampling distortion, edge data redundancy and energy consumption caused by constant sampling frequency of sensors in the IIoT, a data-driven adaptive sampling method based on edge computing is proposed in this paper. The method uses the latest data collected by the sensors at the edge node for linear fitting and adjusts the next sampling frequency according to the linear median jitter sum and adaptive sampling strategy. An edge data acquisition platform is established to verify the validity of the method. According to the experimental results, the proposed method is more effective than other adaptive sampling methods. Compared with constant sampling frequency, the proposed method can reduce the edge data redundancy and energy consumption by more than 13.92% and 12.86%, respectively.
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Registered trials
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