Smart homes

Smart assisted living: A data-centric and machine learning approach to intelligent home systems

November 3, 2025 by Md Zia Uddin

Abstract: The evolution of smart homes is marked as one of the most profound integrations of artificial intelligence (AI) into everyday life. Novelty gadgets or remote-controlled devices no longer define modern intelligent living spaces; instead, they are recognized as cognitive ecosystems capable of perceiving environmental and behavioral cues; learning from routine patterns; … [Read more...] about Smart assisted living: A data-centric and machine learning approach to intelligent home systems

Alexa-based voice assistant for smart home applications

July 1, 2021 by Clara Jiménez, Edgar Saavedra, Guillermo del Campo, and Asunción Santamaría

Today, the Internet of Things (IoT) is becoming an essential player in creating a new smart era. The communication through the Internet to IoT devices enables new applications in multiple environments, including smart buildings and cities. The IoT market is projected to grow to 75.4 billion connected devices by 2020. Within the IoT ecosystem, smart home technology is also … [Read more...] about Alexa-based voice assistant for smart home applications

Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

Smart homes

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