AI powered blockchain framework for predictive temperature control in smart homes using wireless sensor networks and time shifted analysis.

Journal: Scientific reports
Published Date:

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.

Authors

  • Cong Feng
    Department of Emergency, The First Medical Center to Chinese People's Liberation Army General Hospital, Beijing, China.
  • Ahmed Kateb Jumaah Al-Nussairi
    Al-Manara College for Medical Sciences, Amarah, Maysan, Iraq.
  • Mustafa Habeeb Chyad
    College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq.
  • Narinderjit Singh Sawaran Singh
    Faculty of Data Science and Information Technology, INTI International University, 71800, Nilai, Malaysia.
  • Jianyong Yu
    Innovation Center for Textile Science and Technology , Donghua University , Shanghai 200051 , China.
  • Amirfarhad Farhadi
    Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. amir.farhadi@srbiau.ac.ir.

Keywords

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