Artificial Intelligence Driven Approaches to Smart Home Security and Efficiency: A Systematic Review

Authors

  • Muhammed Ibrahim College of Graduate Studies, Universiti Tenaga Nasional, Selangor, Malaysia/ Department of Information System, Nigerian Army University Biu, Borno Nigeria
  • Mohammed A. AI-Sharafi Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
  • Moamin A Mahmoud College of Computing and Informatics; Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Selangor, Malaysia
  • Gamal Alkawsi Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Selangor, Malaysia/ Faculty of Computer Science and Information Systems, Thamar University, Thamar, Yemen
  • Abdulnaser A Hagar Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia

DOI:

https://doi.org/10.46604/aiti.2026.16519

Keywords:

smart home, artificial intelligence, machine learning, energy management, security

Abstract

Artificial intelligence (AI) has emerged as a key technology for improving smart home security and energy efficiency. This study aims to systematically review AI-driven approaches in smart home environments. The review follows the PRISMA 2020 framework and uses the Scopus and Web of Science databases, resulting in 2,429 records, of which 60 studies published between 2021 and 2025 meet the inclusion criteria. The findings indicate that AI significantly enhances smart home performance through automation, energy optimization, anomaly detection, intrusion prevention, and predictive decision-making. Machine learning and deep learning are the most frequently adopted techniques, while reinforcement learning and hybrid AI models demonstrate strong capabilities for adaptive control and energy management. The review also identifies challenges related to data quality, model interpretability, privacy, and real-world implementation. Overall, AI plays a vital role in developing secure, intelligent, and energy-efficient smart home systems that contribute to sustainable urban living and smart city development.

References

A. D. Kounoudes, G. M. Kapitsaki, I. Katakis, and M. Milis, “User-Centred Privacy Inference Detection for Smart Home Devices,” Proceedings of IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/UIC/ATC/ScalCom/IoP/SCI), pp. 210-218, 2021.

R. S. Dixit, S. L. Choudhary, N. Arya, N. Nathani, C. Bhowmik, V. Roy, et al., “Artificial Intelligence-Powered Cloud Security Strategies for Protecting Critical Clinical Operations in Healthcare Environments,” Scientific Reports, 2026.

S. FakhrHosseini, C. Lee, S. H. Lee, and J. Coughlin, “A Taxonomy of Home Automation: Expert Perspectives on the Future of Smarter Homes,” Information Systems Frontiers, vol. 27, no. 2, pp. 449-466, 2025.

I. Muhammed, M. A. Al-Sharafi, M. A. Mahmoud, and N. A. Iahad, “Towards Digital Sustainability: Conceptualizing a Model for Smart Home Adoption and Implementation,” Proceedings of the 29th Pacific Asia Conference on Information Systems (PACIS), Paper no. 2, 2025.

Z. Xu, D. Zhao, Q. Zou, J. Xiao, Y. Jiang, Z. Yuan, et al., “Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes,” IEEE Internet of Things Magazine, vol. 8, no. 6, pp. 17-23, 2025.

N. Butt, A. Shahid, K. N. Qureshi, S. Haider, A. O. Ibrahim, F. Binzagr, et al., “Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks,” Mathematics, vol. 10, no. 23, article no. 4598, 2022.

B. M. Jalal and R. H. Al-Rubayi, “Energy Management Strategy with Smart Building Control System to Reduction Electrical Load Using ANN,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 6, pp. 3188-3200, 2022.

M. Feyzi and H. Mojallali, “Optimal Placement of Light Sensor for Improving Energy Efficiency and Visual Comfort in Smart Buildings,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 9, article no. 100681, 2024.

A. Yarali, “Wireless Sensors/IoT and Artificial Intelligence for Smart Grid and Smart Home,” in Intelligent Connectivity: AI, IoT, and 5G, Hoboken, NJ, USA: John Wiley & Sons, pp. 239-249, 2021.

W. Li, T. Yigitcanlar, A. Liu, and I. Erol, “Mapping Two Decades of Smart Home Research: A Systematic Scientometric Analysis,” Technological Forecasting and Social Change, vol. 179, article no. 121676, 2022.

S. Atiewi, A. Al-Rahayfeh, M. Almiani, A. Abuhussein, and S. Yussof, “Ethereum Blockchain-Based Three Factor Authentication and Multi-contract Access Control for Secure Smart Home Environment in 5G Networks,” Cluster Computing, vol. 27, no. 4, pp. 4551-4568, 2024.

K. R. Jothi and B. Vaithiyanathan, “Developing a Hybrid Approach with Whale Optimization and Deep Convolutional Neural Networks for Enhancing Security in Smart Home Environments’ Sustainability through IoT Devices,” Sustainability, vol. 16, no. 24, article no. 11040, 2024.

H. M. Fayez, G. M. Amer, and E. S. F. El Tantawy, “Load Management in Smart Home Using Intelligent Algorithms,” Proceedings of the 22nd International Middle East Power Systems Conference (MEPCON), pp. 521-527, 2021.

S. Biswas, K. Sharif, Z. Latif, M. J. F. Alenazi, A. K. Pradhan, and A. K. Bairagi, “Blockchain Controlled Trustworthy Federated Learning Platform for Smart Homes,” IET Communications, vol. 18, no. 20, pp. 1840-1852, 2024.

A. R. Dasgupta, P. Kumari, H. Sahu, S. Amarnath, and S. Nanda, “Smart-Home Automation Using AI Assistant and IoT,” Proceedings of the 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), pp. 329-333, 2022.

Y. Wang and L. Liu, “Research on Sustainable Green Building Space Design Model Integrating IoT Technology,” PLOS One, vol. 19, no. 4, article no. e0298982, 2024.

L. Jin and A. Boden, “Review on the Application Areas of Decision-Making Algorithms in Smart Homes,” Frontiers in Artificial Intelligence and Applications, vol. 368, pp. 74-92, 2023.

Y. Sun, S. Zhang, M. Liu, R. Zheng, and S. Dong, “Energy Management Based on Safe Multi-agent Reinforcement Learning for Smart Buildings in Distribution Networks,” Energy and Buildings, vol. 318, article no. 114410, 2024.

A. H. Sodhro, A. Gurtov, N. Zahid, S. Pirbhulal, L. Wang, M. M. Ur Rahman, et al., “Toward Convergence of AI and IoT for Energy-Efficient Communication in Smart Homes,” IEEE Internet of Things Journal, vol. 8, no. 12, pp. 9664-9671, 2021.

S. Sohail, Z. Fan, X. Gu, and F. Sabrina, “Multi-tiered Artificial Neural Networks Model for Intrusion Detection in Smart Homes,” Intelligent Systems with Applications, vol. 16, article no. 200152, 2022.

Z. Wu, X. Chen, Y. Lin, J. Wen, and Y. Chen, “A Smart Home Energy Management System Based on Human Activity Recognition and Deep Reinforcement Learning,” Energy and Buildings, vol. 325, article no. 114951, 2024.

F. Meng and X. Wang, “Application of Energy Scheduling Algorithm Based on Energy Consumption Prediction in Smart Home Energy Scheduling,” Renewable Energy, vol. 231, article no. 120620, 2024.

N. S. Dasappa, G. K. Kumar, and N. Somu, “Multi-Sensor Data Fusion Framework for Energy Optimization in Smart Homes,” Renewable and Sustainable Energy Reviews, vol. 193, article no. 114235, 2024.

Z. Cheng and Z. Yao, “A Novel Approach to Predict Buildings Load Based on Deep Learning and Non-Intrusive Load Monitoring Technique, toward Smart Building,” Energy, vol. 312, article no. 133456, 2024.

L. Langer and T. Volling, “A Reinforcement Learning Approach to Home Energy Management for Modulating Heat Pumps and Photovoltaic Systems,” Applied Energy, vol. 327, article no. 120020, 2022.

U. H. Garba, A. N. Toosi, M. F. Pasha, and S. Khan, “SDN-Based Detection and Mitigation of DDoS Attacks on Smart Homes,” Computer Communications, vol. 221, pp. 29-41, 2024.

M. Y. Chen, “Establishing a Cybersecurity Home Monitoring System for the Elderly,” IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4838-4845, 2022.

R. Singh, K. R. Utkurovich, A. Alkhayyat, G. Saritha, R. Jayadurga, and K. B. Waghulde, “Machine Learning Applications in Energy Management Systems for Smart Buildings,” E3S Web of Conferences, vol. 540, article no. 08002, 2024.

P. Sivagami, D. Jamunarani, P. Abirami, M. Pushpavalli, V. Geetha, and R. Harikrishnan, “Smart Home Automation System Methodologies-A Review,” Proceedings of the International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 1386-1390, 2021.

A. Shokrollahi, J. A. Persson, R. Malekian, A. Sarkheyli-Hägele, and F. Karlsson, “Passive Infrared Sensor-Based Occupancy Monitoring in Smart Buildings: A Review of Methodologies and Machine Learning Approaches,” Sensors, vol. 24, no. 5, article no. 1533, 2024.

E. Alqahtani, N. Janbi, S. Sharaf, and R. Mehmood, “Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling,” Sustainability, vol. 14, no. 20, article no. 13534, 2022.

M. A. Siam, K. Y. Lucky, S. N. Hasan, J. Kaur, H. Kaur, M. S. Uddin, et al., “Cybersecure Intelligent Sensor Framework for Smart Buildings: AI-Based Intrusion Detection and Resilience Against IoT Attacks,” Sensors, vol. 25, no. 24, article no. 7680, 2025.

L. Cao, “AI Science and Engineering: A New Field,” IEEE Intelligent Systems, vol. 37, no. 2, pp. 3-13, 2022.

H. R. O. Rocha, I. H. Honorato, R. Fiorotti, W. C. Celeste, L. J. Silvestre, and J. A. L. Silva, “An Artificial Intelligence Based Scheduling Algorithm for Demand-Side Energy Management in Smart Homes,” Applied Energy, vol. 282, part A, article no. 116145, 2021.

H. Ding, Y. Xu, B. Chew Si Hao, Q. Li, and A. Lentzakis, “A Safe Reinforcement Learning Approach for Multi-Energy Management of Smart Home,” Electric Power Systems Research, vol. 210, article no. 108120, 2022.

P. Rajesh, F. H. Shajin, and G. Kannayeram, “A Novel Intelligent Technique for Energy Management in Smart Home Using Internet of Things,” Applied Soft Computing, vol. 128, article no. 109442, 2022.

I. Wachter, P. Rantuch, and T. Štefko, “Smart Buildings,” in Transparent Wood Materials, Springer Series in Materials Science, vol. 330, Cham, Switzerland: Springer, pp. 87-95, 2023.

A. A. Mahmud, “Artificial Intelligence (AI)-Based Identification of Appliances in Households through NILM,” Proceedings of the 4th Global Power, Energy and Communication Conference (GPECOM), pp. 414-426, 2022.

T. Senapati, G. Chen, and W. Pedrycz, “Artificial Intelligence-Driven Energy Optimization in Smart Homes Using Interval-Valued Fermatean Fuzzy Aczel-Alsina Aggregation Operators,” Journal of Building Engineering, vol. 105, article no. 112418, 2025.

M. R. King, “A Conversation on Artificial Intelligence, Chatbots, and Plagiarism in Higher Education,” Cellular and Molecular Bioengineering, vol. 16, no. 1, pp. 1-2, 2023.

M. A. Khan, A. M. Saleh, M. Waseem, and I. A. Sajjad, “Artificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment,” IEEE Access, vol. 11, pp. 1477-1505, 2023.

W. Li, T. Yigitcanlar, I. Erol, and A. Liu, “Motivations, Barriers and Risks of Smart Home Adoption: From Systematic Literature Review to Conceptual Framework,” Energy Research & Social Science, vol. 80, article no. 102211, 2021.

O. Popoola, M. Rodrigues, J. Marchang, A. Shenfield, A. Ikpehai, and J. Popoola, “A Critical Literature Review of Security and Privacy in Smart Home Healthcare Schemes Adopting IoT & Blockchain: Problems, Challenges and Solutions,” Blockchain: Research and Applications, vol. 5, no. 2, article no. 100178, 2024.

S. F. Wen, A. Shukla, and B. Katt, “Artificial Intelligence for System Security Assurance: A Systematic Literature Review,” International Journal of Information Security, vol. 24, article no. 43, 2025.

C. M. Torres-Hernandez, M. Garduño-Aparicio, and J. Rodriguez-Resendiz, “Smart Homes: A Meta-Study on Sense of Security and Home Automation,” Technologies, vol. 13, no. 8, article no. 320, 2025.

R. Muhammad, M. S. A. Sagara, Y. M. Teluma, and F. A. Wicaksana, “AI as Modern Technology for Home Security Systems: A Systematic Literature Review,” Engineering Proceedings, vol. 107, no. 1, article no. 35, 2025.

M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, et al., “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews,” BMJ, vol. 372, article no. n71, 2021.

M. A. Al-Sharafi, I. Muhammed, S. Alzaeemi, M. A. Albashrawi, I. Chae, and Y. K. Dwivedi, “Factors Shaping FinTech Adoption: A Systematic Review, Key Determinants, Theoretical Insights, Conceptual Framework and Future Research Directions,” Information Discovery and Delivery, 2025.

M. Ibrahim, M. A. Mahmoud, N. Islam, and S. S. Gunasekara, “Enhancing Smart Grid Stability Using AI Techniques: A Systematic Literature Review,” Proceedings of the 21st IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pp. 50-55, 2025.

C. Zhao, X. Wu, P. Hao, Y. Wang, and X. Zhou, “Machine Learning for Optimal Net-Zero Energy Consumption in Smart Buildings,” Sustainable Energy Technologies and Assessments, vol. 64, article no. 103664, 2024.

S. Iram, H. Al-Aqrabi, H. M. Shakeel, H. M. A. Farid, M. Riaz, R. Hill, et al., “An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy Consumption,” IEEE Access, vol. 11, pp. 76300-76320, 2023.

B. Galeb, H. Saad, H. Bashar, K. Al-Majdi, and A. Al-Hilali, “Anomaly Detection in Smart Home Electrical Appliances Using Machine Learning with Statistical Algorithms and Optimized Time Series Algorithms,” Journal of Mechanics of Continua and Mathematical Sciences, vol. 19, no. 5, pp. 116-135, 2024.

A. Srinivasan, V. Parmar, T. Oh, J. Ryoo, and M. Viglione, “Anomaly Detection System for Smart Home Using Machine Learning,” Proceedings of the International Conference on Software Security and Assurance (ICSSA), pp. 52-55, 2021.

T. He and F. Jazizadeh, “Proactive Smart Home Assistants for Automation—User Characteristic-Based Preference Prediction with Machine Learning Techniques,” Proceedings of the ASCE International Conference on Computing in Civil Engineering, pp. 271-278, 2022.

A. Javed, M. N. Awais, A. u. H. Qureshi, M. Jawad, J. Arshad, and H. Larijani, “Embedding Tree-Based Intrusion Detection System in Smart Thermostats for Enhanced IoT Security,” Sensors, vol. 24, no. 22, article no. 7320, 2024.

S. Afroosheh, K. Esapour, R. Khorram-Nia, and M. Karimi, “Reinforcement Learning Layout-Based Optimal Energy Management in Smart Home: AI-Based Approach,” IET Generation, Transmission & Distribution, vol. 18, no. 15, pp. 2509-2520, 2024.

S. J. Chen, W. Y. Chiu, and W. J. Liu, “User Preference-Based Demand Response for Smart Home Energy Management Using Multiobjective Reinforcement Learning,” IEEE Access, vol. 9, pp. 161627-161637, 2021.

X. Ding, A. Cerpa, and W. Du, “Exploring Deep Reinforcement Learning for Holistic Smart Building Control,” ACM Transactions on Sensor Networks, vol. 20, no. 3, pp. 1-28, 2024.

R. Heartfield, G. Loukas, A. Bezemskij, and E. Panaousis, “Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1720-1735, 2021.

W. A. Alonazi, H. Hamdi, N. A. Azim, and A. A. A. El-Aziz, “SDN Architecture for Smart Homes Security with Machine Learning and Deep Learning,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 13, no. 10, pp. 917-927, 2022.

M. Razghandi, H. Zhou, M. Erol-Kantarci, and D. Turgut, “Smart Home Energy Management: VAE-GAN Synthetic Dataset Generator and Q-Learning,” IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 1562-1573, 2024.

R. Sunder, S. R, V. Paul, S. K. Punia, B. Konduri, K. V. Nabilal, et al., “An Advanced Hybrid Deep Learning Model for Accurate Energy Load Prediction in Smart Building,” Energy Exploration & Exploitation, vol. 42, no. 6, pp. 2241-2269, 2024.

K. Yu, Q. Li, D. Chen, and L. Hu, “Safeguarding User-Centric Privacy in Smart Homes,” ACM Transactions on Internet Technology, vol. 24, no. 4, pp. 1-33, 2024.

A. R. Javed, L. G. Fahad, A. A. Farhan, S. Abbas, G. Srivastava, R. M. Parizi, et al., “Automated Cognitive Health Assessment in Smart Homes Using Machine Learning,” Sustainable Cities and Society, vol. 65, article no. 102572, 2021.

F. Ibude, A. Otebolaku, J. E. Ameh, and A. Ikpehai, “Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks,” Journal of Low Power Electronics and Applications, vol. 14, no. 4, article no. 54, 2024.

S. Karamizadeh, M. Moazen, M. Zamani, and A. A. Manaf, “Enhancing IoT-Based Smart Home Security through a Combination of Deep Learning and Self-Attention Mechanism,” Arabian Journal for Science and Engineering, vol. 49, no. 9, pp. 12431-12441, 2024.

R. Sharma, A. Potnis, and V. Chaurasia, “Enhancing Smart Home Security Using Deep Convolutional Neural Networks and Multiple Cameras,” Wireless Personal Communications, vol. 136, no. 4, pp. 2185-2200, 2024.

R. Chambers and M. Fahim, “Healthy Aging: A Proactive Model to Prevent Self-neglecting Behavior in Smart Homes,” Proceedings of the IEEE International Conference on E-Health Networking, Application & Services (HealthCom), pp. 173-178, 2022.

H. Cimen, E. J. Palacios-Garcia, M. Kolaek, N. Cetinkaya, J. C. Vasquez, and J. M. Guerrero, “Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring,” IEEE Industrial Electronics Magazine, vol. 15, no. 2, pp. 4-15, 2021.

C. Sanjay, K. Jahnavi, and S. Karanth, “A Secured Deep Learning Based Smart Home Automation System,” International Journal of Information Technology, vol. 16, pp. 5239-5245, 2024.

A. Bajpai, D. Chaurasia, and N. Tiwari, “A Novel Methodology for Anomaly Detection in Smart Home Networks via Fractional Stochastic Gradient Descent,” Computers and Electrical Engineering, vol. 119, part B, article no. 109604, 2024.

K. P. Prakash, Y. V. P. Kumar, K. Ravindranath, G. Pradeep Reddy, M. Amir, and B. Khan, “Artificial Neural Network-Based Data Imputation for Handling Anomalous Energy Consumption Readings in Smart Homes,” Energy Exploration & Exploitation, vol. 42, no. 4, pp. 1432-1449, 2024.

P. Piruthiviraj, P. Pitchandi, S. Sharma, B. Saroja, G. Rajesh, and P. V. Nandankar, “Automatic Access Control Solution in Smart Homes Using IoT and AI,” AIP Conference Proceedings, vol. 2587, no. 1, article no. 080004, 2023.

S. Bhatlawande, S. Shilaskar, T. Gadad, S. Ghulaxe, and R. Gaikwad, “Smart Home Security Monitoring System Based on Face Recognition and Android Application,” Proceedings of the International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), pp. 222-227, 2023.

A. S. Kumar and R. Rekha, “Improving Smart Home Safety with Face Recognition Using Machine Learning,” Proceedings of the International Conference on Intelligent Systems Communication, IoT and Security (ICISCoIS), pp. 478-482, 2023.

I. Priyadarshini, S. Sahu, R. Kumar, and D. Taniar, “A Machine-Learning Ensemble Model for Predicting Energy Consumption in Smart Homes,” Internet of Things, vol. 20, article no. 100636, 2022.

M. A. Khan, Z. Sabahat, M. S. Farooq, M. Saleem, S. Abbas, M. Ahmad, et al., “Optimizing Smart Home Energy Management for Sustainability Using Machine Learning Techniques,” Discover Sustainability, vol. 5, article no. 430, 2024.

S. T. Spantideas, A. E. Giannopoulos, and P. Trakadas, “Autonomous Price-Aware Energy Management System in Smart Homes via Actor-Critic Learning with Predictive Capabilities,” IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 15018-15033, 2025.

A. Hussain, G. Franchini, M. Akram, M. Ehtsham, M. Hashim, L. Fenili, et al., “Hybrid ML/DL Approach to Optimize Mid-Term Electrical Load Forecasting for Smart Buildings,” Applied Sciences, vol. 15, no. 18, article no. 10066, 2025.

A. Vasudevan, S. I. S. Mohammad, A. A. S. Mohammad, K. I. Al-Daoud, R. M. Batyha, A. Y. AlHadid, et al., “Comprehensive Framework that Can Be Used for Successful Internet of Things Deployment in Smart Home Environments,” in Artificial Intelligence for Agile Business Solutions, M. Alsyasneh and J. Masih, Eds., Cham, Switzerland: Springer Nature Switzerland, pp. 203-214, 2026.

Downloads

Published

2026-08-19

How to Cite

[1]
Muhammed Ibrahim, Mohammed A. AI-Sharafi, Moamin A Mahmoud, Gamal Alkawsi, and Abdulnaser A Hagar, “Artificial Intelligence Driven Approaches to Smart Home Security and Efficiency: A Systematic Review”, Adv. technol. innov., Aug. 2026.

Issue

Section

Articles