Smart_Home_Device_Dataset

Citation Author(s):
KR
K M
Submitted by:
K M Karthick Ra...
Last updated:
Tue, 03/11/2025 - 02:17
DOI:
10.21227/dtv7-nc17
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Abstract 

The Smart Home Device Dataset consists of 5000 samples collected at an hourly interval starting from January 2022, representing consumer electronics and IoT-enabled devices in a home automation environment. Each entry is associated with a unique device ID, ensuring identification of distinct devices. The dataset captures real-time sensor readings, including temperature variations (18°C to 30°C), power consumption levels (10W to 500W), and user activity states (Active, Idle, or Sleep), which provide contextual insights into device operation. Additionally, the dataset logs device modes (Auto, Manual, Standby) to indicate system settings during operation. The anomaly flag denotes potential system failures, with a 5% anomaly rate ensuring a realistic distribution for anomaly detection modeling. The decision label categorizes each sample into Normal, Warning, or Critical states, serving as a ground truth for intelligent decision synthesis in AI models. The dataset is designed for optimal decision-making, and adaptive control optimization, enabling real-time AI-driven automation strategies. The data is diverse, structured, and ethically compliant, ensuring generalizability across different environments.

Instructions: 

The Smart Home Device Dataset has been meticulously designed to align with ethical standards in AI data collection, ensuring privacy, fairness, and practical applicability. The dataset consists of non-personally identifiable data, meaning no sensitive user information is stored or processed, ensuring compliance with data protection regulations. The attributes, including temperature, power consumption, device mode, and user activity states, are collected in an aggregated and anonymized manner, reflecting realistic smart home environments without violating individual privacy. Additionally, fair data distribution strategies have been applied, ensuring that the dataset is not biased toward specific device types, user behaviors, or operational modes, making it universally generalizable for AI-driven home automation research. The inclusion of anomaly flags and decision labels ensures a realistic representation of device states, allowing AI models to learn from ethically collected, unbiased, and practically relevant data.