Feng Cong, Jumaah Al-Nussairi Ahmed Kateb, Chyad Mustafa Habeeb, Sawaran Singh Narinderjit Singh, Yu Jianyong, Farhadi Amirfarhad
School of Computer Science and Engineering, Hunan University of Information Technology, Changsha, 410151, China.
Al-Manara College for Medical Sciences, Amarah, Maysan, Iraq.
Sci Rep. 2025 May 25;15(1):18168. doi: 10.1038/s41598-025-03146-w.
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.
在智能家居环境中，高效管理温度控制，同时优化能源消耗并确保数据安全，仍然是一项重大挑战。传统的基于恒温器的系统缺乏预测能力，在高峰时段能源消耗往往会激增，导致效率低下。此外，智能家居环境中敏感数据的安全问题日益受到关注。本文提出了一种新颖的基于人工智能的区块链框架，用于智能家居中的预测性温度控制，该框架利用无线传感器网络（WSN）和时移分析。该框架集成了用于预测温度管理的机器学习（ML）算法、用于安全数据处理的区块链技术以及用于实时数据处理的边缘计算，从而形成了一个高效且安全的系统。关键创新包括动态检测加热和冷却事件、基于历史数据的预测调度以及基于区块链的去中心化能源交易。性能评估表明，该系统能够以28.5%的成功率准确检测散热器加热开启事件，而散热器冷却事件检测的准确率达到37.3%。计划的加热开启事件触发可靠性为68.4%，与传统恒温器控制相比，该系统的机器学习组件通过基于预测分析调整加热，成功将能源消耗降低了15.8%。此外，时移数据处理将高峰时段的计算负载降低了22%，有助于提高整体能源效率和系统可扩展性。区块链的集成确保了数据安全不可篡改，消除了未经授权的数据访问，并提高了智能家居环境中的信任度。这些结果表明，结合人工智能、区块链和无线传感器网络创建一个强大、节能且安全的智能家居温度控制系统具有潜力，与传统解决方案相比有显著改进。
