Data Science and Smart Living

Dear Colleagues,

We are now entering a comprehensive and deeply integrated digital era. From collaborative filtering recommendations in the age of e-commerce to human–machine dialogue powered by large language models, data-driven technological innovation has demonstrated remarkable cognitive capabilities in improving both production and everyday life. Data serves as the critical “nourishment” that enables technologies to better understand human behavior, society, the economy, and the world's complexity, thus driving the evolution from automation to intelligence. Consequently, the pursuit of technological advancement has shifted from a focus on efficiency enhancement to a broader ambition of enabling higher-quality, more inclusive smart living.

Although the existing literature on smart technologies and the digital economy is substantial, the potential value embedded in data goes far beyond current levels of exploitation and exploration. Today, there is a growing need for more advanced data science techniques to unlock the full potential of data resources—particularly in the cleaning, training, and augmentation of multimodal data. These capabilities are crucial for further empowering intelligent technologies across various industries and enhancing public well-being. Data science is fundamentally concerned with the development and application of scientific methods, processes, algorithms, and systems to extract valuable insights and knowledge from both structured and unstructured data. These insights support data-driven decision-making for both organizations and individuals.

In light of these concerns, this Special Issue aims to invite comprehensive studies that provide insights into the role of data science in smart living, encouraging the application of AI-enabled data analytics in decision support.

We believe that the collective contributions from this issue will provide valuable policy insights for decision-makers who govern societal innovations in digitalization, as well as practical guidance for industry professionals engaged in and benefiting from technology-driven business evolution.

Topics of interest include, but are not limited to, the following:

• Exploratory Discussion on 6th Generation Wireless Systems.
• Artificial Intelligence of Things (AIoT): Technology and devices (embedded systems, deep learning, cloud and edge computing, etc.), efficiency and scenario advancement, security and ethnic challenges, etc.
• Multimodal Data Fusion.
• Machine Learning as a Service (MLaaS), Artificial Intelligence as a Service (AIaaS), and Intelligence as a Service (IQaaS): Fundamental mechanisms, specific technological scenarios, and smart living effectiveness (e.g., machine and computer vision, cognitive computing, intelligent analytics, code-free AI operations, etc.).
• Vehicle-to-Everything (V2X): Types of communication, technical route, commercial applications, etc.
• Smart Low-altitude Economy: Computation contexts (e.g., intelligent sensing and decision support, dynamic and real-time autonomous path optimization in complex environments, deep reinforcement learning, big data analytics, etc.) and smart living applications (e.g., low-altitude sightseeing, intelligent transportation, precision delivery, emergency rescue, risk reconnaissance, etc.).
• Digital Twins Applied to Smart Living: Technical studies (e.g., full life cycle data, data-based physical devices, modelling and simulation, mixed reality); scenario studies (e.g., smart manufacturing, city planning and management, smart energy systems, smart healthcare, etc.).
• Smart City and Smart Agriculture.
• Data-driven Sustainability: Digitalized resource and energy management system, data science in addressing climate challenges, computation network and regional cooperation, big data analytics and public health, etc.
• Business Technology Iteration and Invention: Technology exploration through theoretical deduction or software simulation. Methodological articles proposing new research techniques are also acceptable, but they should provide detailed theoretical frameworks or follow tutorial formats.
• Smart Economy Forecasting: The diffusion trends of technological breakthroughs and the potential of emerging business phenomena. Compelling evidence is required to justify the validity of the estimations.

Prof. Dr. Peng Zhu
Prof. Dr. Da Huo
Guest Editors

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• data science
• smart living
• next generation technologies
• technology fusion and contextualization
• smart economy
• smart service
• smart technology and society