⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

January 1, 2025International Journal of Sensor Networks

A Univariate Time-series Anomaly Detection Method for Sensor Network based on Conditional Variational AutoEncoder

Ping He Zhifeng Wu Sirui Hao Hao Liu Jiangchuan Chen Xuefeng Song Yipeng Liu Junjiang He

View at Publisher

Ask AI

Bookmark

Share

Key Points

The research aims to develop a method for detecting anomalies in univariate time-series data from sensor networks.
Developed a univariate time-series anomaly detection technique.
Utilized conditional variational autoencoders for analysis.
Tested the method on sensor network data.
Demonstrated effectiveness in detecting anomalies in time-series data.
Achieved higher accuracy compared to traditional methods.
Showed improved reliability in monitoring sensor networks.

Implication

A univariate time-series method detects anomalies in sensor networks, indicating more robust monitoring systems.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

He et al. (2025) studied this question.

synapsesocial.com/papers/69ada836bc08abd80d5bb49f https://doi.org/10.1504/ijsnet.2025.10076827

View at Publisher

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1An efficient intrusion detection system using unsupervised learning AutoEncoderOriginal Research · 2026
2TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data2024
3Research on anomaly detection in energy engineering bidding based on spatiotemporal graph neural networkOriginal Research · 2026
4Graph neural network-driven anomaly detection framework for UAV inspection dataOriginal Research · 2026
5Disentangled Anomaly Detection For Multivariate Time Series2024 · 9 citations

More related papers

An Anomaly Detection Enhancement Method Using Interpretable Unsupervised Machine Learning in Industrial Information Systems at Multivariate Time Series Environment2024
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection2016
Anomaly Detection in Autonomous Vehicle's Lidar Sensor Data Using Variational Autoencoders2024

More from International Journal of Sensor Networks

Adaptive Blockchain-based Energy Trading for Distributed Electric Vehicle (EV) Charging Networks in Urban Smart GridsThu,
CAPS6: Context Aware Parent Node Selection in 6TiSCH Network based Industrial Internet of ThingsThu,