Abstract
"500 simulation scenarios analyzing dynamic optimization techniques for energy efficiency in IoT sensor networks. Includes network lifetime, PDR, and energy consumption metrics."
Description
Dataset Description
This dataset evaluates the impact of dynamic optimization techniques on energy efficiency in IoT sensor networks through 500 controlled simulation scenarios.
Features (13 columns)
- Network Parameters: Nodes, Area (m²), Initial Energy (J), Transmission Rate (pps)
- Optimization: Traditional vs ICSHSO-Based routing
- Performance Metrics: Network lifetime improvement (%), Energy consumption reduction (%), Packet Delivery Ratio (PDR %), End-to-End Delay (ms)
- Target: Optimization Effectiveness (0 or 1)
Applications
Ideal for ML classification models, routing protocol benchmarking, and energy-efficient IoT network design.
Data Preview
| Simulation_ID | Nodes | Area_m2 | Initial_Energy_J | Transmission_Rate_pps | Optimization_Technique | Network_Lifetime_Improvement_% | Energy_Consumption_Reduction_% | PDR_% | End_to_End_Delay_ms | Residual_Energy_Utilization_% | Reduction_in_Redundant_Transmissions_% | Optimization_Effective |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 50 | 100x100 | 0.5 | 10 | ICSHSO-Based | 45.2 | 38.7 | 95.3 | 12.5 | 87.2 | 42.1 | 1 |
| 2 | 75 | 150x150 | 0.7 | 15 | Traditional | 12.3 | 8.9 | 78.4 | 28.3 | 65.1 | 15.2 | 0 |
Showing first few rows for preview
Cite This Dataset
Ziya (2025). IoT Sensor Network Energy Optimization Dataset. [Dataset]. Kaggle. https://www.kaggle.com/datasets/ziya07/iot-sensor-network-dataset
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Original source: Kaggle (2025). Visit official page for more details.
Indexed by IoTDataset.com on Jan 21, 2026
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