Multi-Tier IoT Resource Allocation Dataset for Performance Analytics
Catalog Summary
"A dataset capturing real-time metrics of resource allocation and workload distribution across multi-tier IoT architectures. Includes latency, CPU and memory usage, task execution times, and predictive performance variables, enabling research in IoT resource management, edge analytics and performance optimization."
Catalog Notes
Overview
This dataset records key resource metrics across IoT computing tiers (device, edge, fog, cloud) and supports research into latency, jitter, and predictive analytics for efficient service management.
Dataset Contents
- Data modality: Time-series performance metrics
- Scale: Device and tier metrics
- File formats: CSV
- Metadata: Timestamps, tier IDs
Collection Methodology
- Environment: Multi-tier IoT stack
- Duration: Continuous operation
- Devices/Sensors: Performance counters
- Sampling rate: Configured time intervals
Labels and Targets
- Labeling: Resource usage states
- Ground truth: Recorded system logs
Recommended Use Cases- Performance modeling
- Edge computing research
- Predictive analytics
Limitations and Considerations- Size is moderate for large-scale training
Access and Licensing
- Size is moderate for large-scale training
Access and Licensing
This dataset is available at official repository page. Licensed under CC BY 4.0.
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on KaggleCite This Dataset
Ziya, U. (2025). Multi-Tier IoT Resource Allocation Dataset for Performance Analytics. [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17789829
Source metadata: Zenodo (2025) · DOI: 10.5281/zenodo.17789829
Indexed by IoTDataset.com on Feb 07, 2026
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