This study evaluates the current scope of smart technology applications that support aging in place and identifies potential avenues for future research. The global demographic shift towards an aging population has intensified interest in technologies that enable older adults to maintain independence and quality of life within their homes. We conducted a systematic review of the scientific literature from Web of Science, PubMed, and ProQuest, identifying 44 smart technologies across 32 publications. These technologies were classified into three categories: nonmobile technologies for individual monitoring, nonmobile technologies for home environment monitoring, and wearable technologies for health and activity tracking. Notably, the research in this area has grown significantly since 2018; yet, notable gaps persist, particularly within the traditional disciplines related to aging and in the use of quantitative methodologies. This emerging field presents substantial opportunities for interdisciplinary research and methodological advancement, highlighting the need for well-developed research strategies to support the effective integration of smart technology in aging in place.

This review paper examines how Generative AI (GAI) and Large Language Models (LLMs) can transform smart cities in the Industry 5.0 era. Through selected case studies and portions of the literature, we analyze these technologies’ impact on industrial processes and urban management. The paper targets GAI as an enabler for industrial optimization and predictive maintenance, underlining how domain experts can work with LLMs to improve municipal services and citizen communication, while addressing the practical and ethical challenges in deploying these technologies. We also highlight promising trends, as reflected in real-world case studies ranging from factories to city-wide test-beds and identify pitfalls to avoid. Widespread adoption of GAI still faces challenges that include infrastructure and lack of specialized knowledge as a limitation of proper implementation. While LLMs enable new services for citizens in smart cities, they also expose certain privacy issues, which we aim to investigate in this study. Finally, as a way forward, the paper suggests future research directions covering new ethical AI frameworks and long-term studies on societal impacts. Our paper is a starting point for industrial pioneers and urban developers to navigate the complexity of GAI and LLM integration, balancing the demands of technological innovation on one hand and ethical responsibility on the other.

Behavior mapping is the systematic observation of people using their environments. The Smart Social Spaces research project, recently completed in Sydney, Australia, is used as a vehicle to illustrate the usefulness of this method for understanding the relationships between people and public spaces in cities. Behavior mapping was the central method used to establish what impact the inclusion of smart technology and street furniture had on people’s use of two public spaces. Using this method, it is possible to record real-time patterns of people’s use of public space, enabling local authorities to better support the social use of public space and the management of its infrastructure.