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Cescpra: A Cloud-Edge-Sensor Collaborative Proactive Reliability Assurance Technology
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AI
DOI：10.1109/JSEN.2024.3521482.png)
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Abstract
En 中文
In the Internet of Everything (IoE) era, various sensors configured at the Internet of Things (IoT) edge (Edge) collect massive amounts of data daily. Hard drives are the most failure-prone component in Edge, leading to service unavailability and the permanent loss of sensor-collected data. To solve this problem, we propose Cescpra, a cloud-edge-sensor collaborative proactive reliability assurance technology. Sensors collect data and store it at the Edge using erasure coding, with data and parity blocks stored separately on the Edge and in the cloud, periodically updated asynchronously. To enhance reliability, we propose a two-level model strategy for hard drive failure prediction, with lightweight models on each device and a full-weight model in the cloud. Samples predicted as suspected faults by the lightweight model will be sent to the cloud for final accurate prediction. Upon confirming imminent failure, proactive data recovery through migration and rebuild is initiated. In an experimental setup with a three-node cloud server, 42 IoT edges, and 210 sensors, our hard drive failure prediction model achieved a true-positive rate (TPR) of 99%, an area under the curve (AUC) score of 0.999, and a false-positive rate (FPR) below 0.01%, minimizing unnecessary data recovery. The multidisk joint proactive recovery method reduced fault tolerance time by up to 64.7% and 62.5% compared to individual migration or rebuild methods. This cloud-edge-sensor collaborative architecture leverages Edge's computational and storage capabilities, significantly enhancing the reliability of sensor data.
Keywords:
Sensors
Cloud computing
Hard disks
Reliability
Encoding
Collaboration
Computer architecture
Predictive models
Metadata
Accuracy
Cloud-edge-sensor collaborative
erasure coding
failure prediction
reliability of sensor data
time series characteristics
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W