DOI:
https://doi.org/10.64539/sjcs.v2i2.2026.479Keywords:
Internet of Things, Smart Home Automation, System Architecture, Machine Learning, PRISMA FrameworkAbstract
The fast adoption of Internet of Things (IoT) technologies in smart home has driven the demand for secure, smart and energy efficient homes. However, findings from previous research were limited. This study aims at addressing the growing demand for common and scalable solutions in next generation smart home environments by performing a systematic literature review of smart home systems featuring IoT. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA 2020) framework was used in the study. The literature was collected from major peer-reviewed academic databases. The Assessments published during the period 2021-2026 were selected for qualitative synthesis after screening, eligibility and quality evaluation. The five main dimensions were analyzed: architectural trends, communication trends, security and privacy mechanisms, Human Activity Recognition (HAR) and intelligent automation, energy management strategies, and research challenges. Results reveal that smart home systems are increasingly multi-layer and hybrid edge-cloud systems based on technologies like Wireless Fidelity (Wi-Fi), ZigBee, Bluetooth Low Energy (BLE), Long Range (LoRa), and Z-Wave. Typical applications for Machine Learning (ML) and Deep Learning (DL) include energy optimisation (forecasting, reinforcement learning), as well as intrusion detection, automation, and context-aware decision making. Challenges faced are interoperability issues, cyber security concerns, computational problems, device variations, and lack of real-world testing. The aim of the study is to create an integrated synthesis and comparative taxonomy that can guide the future development of scalable, secure and intelligent smart home ecosystems.
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