ArticleScientific reports2025
AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Building sensor coverage in couture: balancing cost, coverage, and comfort.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the context of smart homes, efficiently managing temperature control while optimizing energy consumption and ensuring data security remains a significant challenge. Traditional thermostat-based systems lack predictive capabilities, and energy consumption often spikes during peak hours, leading to inefficiency. Additionally, the security of sensitive data in smart home environments is a growing concern. This paper presents a novel AI-powered blockchain framework for predictive temperature control in smart homes, leveraging wireless sensor networks (WSNs) and time-shifted analysis. The framework integrates machine learning (ML) algorithms for predictive temperature management, blockchain technology for secure data handling, and edge computing for real-time data processing, resulting in a highly efficient and secure system. Key innovations include the dynamic detection of heating and cooling events, predictive scheduling based on historical data, and blockchain-based decentralized energy trading. Performance evaluation demonstrates that the system accurately detects radiator heat-on events with a 28.5% success rate, while radiator cooling event detection achieves 37.3% accuracy. Scheduled heat-on events were triggered with 68.4% reliability, and the system's machine learning component successfully reduced energy consumption by 15.8% compared to traditional thermostat controls, by adjusting heating based on predictive analysis. Additionally, the time-shifted data processing reduces peak-time computational load by 22%, contributing to overall energy efficiency and system scalability. The integration of blockchain ensures tamper-proof data security, eliminating unauthorized data access, and improving trust in smart home environments. These results illustrate the potential of combining AI, blockchain, and WSNs to create a robust, energy-efficient, and secure smart home temperature control system, offering significant improvements over traditional solutions.
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Registered trials
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