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Multi-Tier IoT Resource Allocation Dataset for Performance Analytics

IoT Sensors
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Abstract

"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."

Description

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

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 Kaggle

Cite This Dataset 

Ziya, Unknown (2025). Multi-Tier IoT Resource Allocation Dataset for Performance Analytics. [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.17789829

Source: Zenodo (2025) · DOI: 10.5281/zenodo.17789829

Indexed by IoTDataset.com on Feb 07, 2026

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Related Topics & Keywords

#iot sensors #resource allocation #performance metrics #edge computing #latency #dataset #predictive analytics

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