The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed framework employs machine learning and deep learning methods – including Random Forests, Autoencoders, and Long Short-Term Memory (LSTM) networks – to analyze multi-sensor data, detect anomalies, and predict failures before they occur. A simulated smart home environment demonstrated over 90% fault-prediction accuracy, leading to measurable reductions in energy consumption and maintenance costs. Ethical considerations such as data privacy, transparency, and algorithmic fairness are explored. Findings indicate that AI-driven PdM significantly enhances operational continuity, sustainability, and user comfort, paving the way toward autonomous, self-healing smart homes. Future research directions include federated learning, digital twins, edge AI, uncertainty-aware models, and adaptive self-learning systems.

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1. Ahn J, Lee Y, Kim N, Park C, Jeong J. Federated learning for predictive maintenance and anomaly detection using time series data distribution shifts in manufacturing processes. Sensors. 2023 Aug 22;23(17):7331.
2. Reis MJ, Serôdio C. Edge AI for real-time anomaly detection in smart homes. Future Internet. 2025 Apr 18;17(4):179.
3. Njor E, Hasanpour MA, Madsen J, Fafoutis X. A holistic review of the TinyML stack for predictive maintenance. IEEE Access. 2024 Dec 9.
4. Mateus BC, Mendes M, Farinha JT, Martins A. Hybrid deep learning for predictive maintenance: LSTM, GRU, CNN, and dense models applied to transformer failure forecasting. Energies. 2025 Oct 27;18(21):5634.
5. Bampoula X, Nikolakis N, Alexopoulos K. Condition monitoring and predictive maintenance of assets in manufacturing using LSTM-autoencoders and transformer encoders. Sensors. 2024 May 18;24(10):3215.
6. Ramadan MN, Ali MA, Jaber H, Alkhedher M. Blockchain-secured IoT–federated learning for industrial air pollution monitoring: A mechanistic approach to exposure prediction and environmental safety. Ecotoxicol Environ Saf. 2025 Jul 15;300:118442.
7. Hu W, Wang X, Tan K, Cai Y. Digital twin-enhanced predictive maintenance for indoor climate: A parallel LSTM-autoencoder failure prediction approach. Energy Build. 2023 Dec 15;301:113738.
8. Zhong D, Xia Z, Zhu Y, Duan J. Overview of predictive maintenance based on digital twin technology. Heliyon. 2023 Apr 1;9(4).
9. Katib I, Albassam E, Sharaf SA, Ragab M. Safeguarding IoT consumer devices: Deep learning with TinyML-driven real-time anomaly detection for predictive maintenance. Ain Shams Eng J. 2025 Feb 1;16(2):103281.
10. Naik N, Surendranath N, Raju SA, Madduri C, Dasari N, Shukla VK, et al. Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in healthcare Internet of Things environments. Sci Rep. 2025 Aug 23;15(1):31039.
11. Ahmad R, Kamaruddin S. An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng. 2012 Aug 1;63(1):135–49.
12. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997 Nov 15;9(8):1735–80.
13. Lin H, Bergmann NW. IoT privacy and security challenges for smart home environments. Information. 2016 Jul 13;7(3):44.
14. Malhotra P. LSTM-based encoder–Decoder for multi-sensor anomaly detection. arXiv. 2016;arXiv:1607.00148.
15. Zhang W, Yang D, Wang H. Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst J. 2019 May 6;13(3):2213–27.
16. Fuller A, Fan Z, Day C, Barlow C. Digital twin: Enabling technologies, challenges and open research. IEEE Access. 2020 May 28;8:108952–71.
17. Lai F, Dai Y, Singapuram S, Liu J, Zhu X, Madhyastha H, et al. FedScale: Benchmarking model and system performance of federated learning at scale. In: Proc Int Conf Mach Learn. 2022 Jun 28;11814–27.
18. Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: Vision and challenges. IEEE Internet Things J. 2016 Jun 9;3(5):637–46.
19. Rigaki M, Garcia S. A survey of privacy attacks in machine learning. ACM Comput Surv. 2023 Nov 10;56(4):1–34.
20. Palensky P, Dietrich D. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform. 2011 Jun 27;7(3):381–8.

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has transformed smart homes into intelligent, self-regulating environments. Despite these advancements, ensuring the long-term reliability and efficiency of interconnected devices remains a complex challenge due to device heterogeneity, dynamic usage patterns, and continuous data streams. This study investigates AI-driven predictive maintenance (PdM) as a proactive strategy for detecting early signs of equipment degradation in residential settings. The proposed framework employs machine learning and deep learning methods – including Random Forests, Autoencoders, and Long Short-Term Memory (LSTM) networks – to analyze multi-sensor data, detect anomalies, and predict failures before they occur. A simulated smart home environment demonstrated over 90% fault-prediction accuracy, leading to measurable reductions in energy consumption and maintenance costs. Ethical considerations such as data privacy, transparency, and algorithmic fairness are explored. Findings indicate that AI-driven PdM significantly enhances operational continuity, sustainability, and user comfort, paving the way toward autonomous, self-healing smart homes. Future research directions include federated learning, digital twins, edge AI, uncertainty-aware models, and adaptive self-learning systems.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Ahn J, Lee Y, Kim N, Park C, Jeong J. Federated learning for predictive maintenance and anomaly detection using time series data distribution shifts in manufacturing processes. Sensors. 2023 Aug 22;23(17):7331.
2. Reis MJ, Serôdio C. Edge AI for real-time anomaly detection in smart homes. Future Internet. 2025 Apr 18;17(4):179.
3. Njor E, Hasanpour MA, Madsen J, Fafoutis X. A holistic review of the TinyML stack for predictive maintenance. IEEE Access. 2024 Dec 9.
4. Mateus BC, Mendes M, Farinha JT, Martins A. Hybrid deep learning for predictive maintenance: LSTM, GRU, CNN, and dense models applied to transformer failure forecasting. Energies. 2025 Oct 27;18(21):5634.
5. Bampoula X, Nikolakis N, Alexopoulos K. Condition monitoring and predictive maintenance of assets in manufacturing using LSTM-autoencoders and transformer encoders. Sensors. 2024 May 18;24(10):3215.
6. Ramadan MN, Ali MA, Jaber H, Alkhedher M. Blockchain-secured IoT–federated learning for industrial air pollution monitoring: A mechanistic approach to exposure prediction and environmental safety. Ecotoxicol Environ Saf. 2025 Jul 15;300:118442.
7. Hu W, Wang X, Tan K, Cai Y. Digital twin-enhanced predictive maintenance for indoor climate: A parallel LSTM-autoencoder failure prediction approach. Energy Build. 2023 Dec 15;301:113738.
8. Zhong D, Xia Z, Zhu Y, Duan J. Overview of predictive maintenance based on digital twin technology. Heliyon. 2023 Apr 1;9(4).
9. Katib I, Albassam E, Sharaf SA, Ragab M. Safeguarding IoT consumer devices: Deep learning with TinyML-driven real-time anomaly detection for predictive maintenance. Ain Shams Eng J. 2025 Feb 1;16(2):103281.
10. Naik N, Surendranath N, Raju SA, Madduri C, Dasari N, Shukla VK, et al. Hybrid deep learning-enabled framework for enhancing security, data integrity, and operational performance in healthcare Internet of Things environments. Sci Rep. 2025 Aug 23;15(1):31039.
11. Ahmad R, Kamaruddin S. An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng. 2012 Aug 1;63(1):135–49.
12. Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997 Nov 15;9(8):1735–80.
13. Lin H, Bergmann NW. IoT privacy and security challenges for smart home environments. Information. 2016 Jul 13;7(3):44.
14. Malhotra P. LSTM-based encoder–Decoder for multi-sensor anomaly detection. arXiv. 2016;arXiv:1607.00148.
15. Zhang W, Yang D, Wang H. Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst J. 2019 May 6;13(3):2213–27.
16. Fuller A, Fan Z, Day C, Barlow C. Digital twin: Enabling technologies, challenges and open research. IEEE Access. 2020 May 28;8:108952–71.
17. Lai F, Dai Y, Singapuram S, Liu J, Zhu X, Madhyastha H, et al. FedScale: Benchmarking model and system performance of federated learning at scale. In: Proc Int Conf Mach Learn. 2022 Jun 28;11814–27.
18. Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: Vision and challenges. IEEE Internet Things J. 2016 Jun 9;3(5):637–46.
19. Rigaki M, Garcia S. A survey of privacy attacks in machine learning. ACM Comput Surv. 2023 Nov 10;56(4):1–34.
20. Palensky P, Dietrich D. Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform. 2011 Jun 27;7(3):381–8.