Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks

• URL: http://arxiv.org/abs/2204.09942v2
• Date: Thu, 18 Sep 2025 12:21:26 GMT
• Title: Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks
• Authors: Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang,
• Abstract summary: This paper develops a cloud-edge collaborative data anomaly detection approach for industrial sensor networks.<n>It consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud.
• Score: 14.787562130002557
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. Thus, all sensor data have to be uploaded to the control center for analysis, leading to a heavy traffic load. However, industrial sensor networks have high requirements for reliable and real-time communication. The heavy traffic load may cause communication delays or packets lost by corruption. Second, there are complex spatial and temporal features in industrial sensor data. The full extraction of such features plays a key role in improving detection performance.To solve the limitations above, this paper develops a cloud-edge collaborative data anomaly detection approach for industrial sensor networks that mainly consists of a sensor data detection model deployed at individual edges and a sensor data analysis model deployed in the cloud. The former is implemented using Gaussian and Bayesian algorithms, which effectively filter the substantial volume of sensor data generated during the normal operation of the industrial sensor network, thereby reducing traffic load. It only uploads all the sensor data to the sensor data analysis model for further analysis when the network is in an anomalous state. The latter based on GCRL is developed by inserting Long Short-Term Memory network (LSTM) into Graph Convolutional Network (GCN), which can effectively extract the spatial and temporal features of the sensor data for anomaly detection.

Related papers

• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• A Variational Autoencoder Framework for Robust, Physics-Informed Cyberattack Recognition in Industrial Cyber-Physical Systems [2.051548207330147]
  We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on industrial control systems. The framework has a hybrid design that combines a variational autoencoder (VAE), a recurrent neural network (RNN), and a Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2023-10-10T19:07:53Z)
• Deep convolutional neural networks for cyclic sensor data [0.0]
  This study focuses on sensor-based condition monitoring and explores the application of deep learning techniques. Our investigation involves comparing the performance of three models: a baseline model employing conventional methods, a single CNN model with early sensor fusion, and a two-lane CNN model (2L-CNN) with late sensor fusion.
  arXiv  Detail & Related papers  (2023-08-14T07:51:15Z)
• Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data [69.25738064847175]
  It is necessary to consider the behavior of the signals in each sensor separately, to take into account their correlation and hidden relationships with each other. The graph nodes can be represented as data from the different sensors, and the edges can display the influence of these data on each other. It was proposed to construct a graph during the training of graph neural network. This allows to train models on data where the dependencies between the sensors are not known in advance.
  arXiv  Detail & Related papers  (2022-10-20T11:03:21Z)
• Anomaly Detection and Inter-Sensor Transfer Learning on Smart Manufacturing Datasets [6.114996271792091]
  In many cases, the goal of the smart manufacturing system is to rapidly detect (or anticipate) failures to reduce operational cost and eliminate downtime. This often boils down to detecting anomalies within the sensor date acquired from the system. The smart manufacturing application domain poses certain salient technical challenges. We show that predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2022-06-13T17:51:24Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• From One to Many: A Deep Learning Coincident Gravitational-Wave Search [58.720142291102135]
  We construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors.
  arXiv  Detail & Related papers  (2021-08-24T13:25:02Z)
• Fast Wireless Sensor Anomaly Detection based on Data Stream in Edge Computing Enabled Smart Greenhouse [5.716360276016705]
  Edge computing enabled smart greenhouse is a representative application of Internet of Things technology. Traditional anomaly detection algorithms have not properly considered the inherent characteristics of data stream produced by wireless sensor.
  arXiv  Detail & Related papers  (2021-07-28T13:32:12Z)
• Radar Voxel Fusion for 3D Object Detection [0.0]
  This paper develops a low-level sensor fusion network for 3D object detection. The radar sensor fusion proves especially beneficial in inclement conditions such as rain and night scenes.
  arXiv  Detail & Related papers  (2021-06-26T20:34:12Z)
• On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks [25.838878314196375]
  We evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate. The final objective is to identify the optimal setup that would minimize the amount of data to be distributed over the channel.
  arXiv  Detail & Related papers  (2021-04-23T18:58:37Z)
• Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems [4.193524211159057]
  In this paper, we analyze data from sensors deployed in an agricultural farm with data from seven different kinds of sensors, and from an advanced manufacturing testbed with vibration sensors. We show how in these two application domains, predictive failure classification can be achieved, thus paving the way for predictive maintenance.
  arXiv  Detail & Related papers  (2021-02-11T02:37:27Z)
• Real-time detection of uncalibrated sensors using Neural Networks [62.997667081978825]
  An online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. The solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively.
  arXiv  Detail & Related papers  (2021-02-02T15:44:39Z)
• Data-aided Sensing for Gaussian Process Regression in IoT Systems [48.523643863141466]
  We use data-aided sensing to learn data sets collected from sensors in Internet-of-Things systems. We show that modified multichannel ALOHA with predictions can help improve the performance of Gaussian process regression with data-aided sensing.
  arXiv  Detail & Related papers  (2020-11-23T20:59:51Z)
• Deep Learning based Covert Attack Identification for Industrial Control Systems [5.299113288020827]
  We develop a data-driven framework that can be used to detect, diagnose, and localize a type of cyberattack called covert attacks on smart grids. The framework has a hybrid design that combines an autoencoder, a recurrent neural network (RNN) with a Long-Short-Term-Memory layer, and a Deep Neural Network (DNN)
  arXiv  Detail & Related papers  (2020-09-25T17:48:43Z)
• Data-Driven Construction of Data Center Graph of Things for Anomaly Detection [5.160640187262777]
  Data center (DC) contains both IT devices and facility equipment, and the operation of a DC requires a high-quality monitoring system. There are lots of sensors in computer rooms for the DC monitoring system, and they are inherently related. This work proposes a data-driven pipeline to build a DC graph of things (sensor graph) from the time series measurements of sensors.
  arXiv  Detail & Related papers  (2020-04-27T01:54:43Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.