IoT-RL: 20,000 Transitions for Solar, Wind, and Adaptive IoT Transmission Control

20,000 reinforcement learning transitions for IoT device control, focusing on battery management and solar or wind energy harvesting. The dataset was published on Kaggle and is associated with artificial intelligence and deep learning applications. The author, organization, and last update date are not specified.

Use Cases

• Train reinforcement learning agents for battery charge/discharge control based on energy harvesting data.
• Develop adaptive transmission policies for IoT devices based on available solar or wind power.
• Benchmark RL algorithms for resource-constrained systems using the 20,000 provided transitions.

Strengths

• Dataset contains 20,000 reinforcement learning transitions, providing a substantial base for training.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Kaggle

Collection Method

Method of data gathering is not described.