Smart homes in the Era of Sustainability and Digitalization: A Bibliometric Review

Smart homes in the Era of Sustainability and Digitalization: A Bibliometric Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Smart homes in the Era of Sustainability and Digitalization: A Bibliometric Review Arash Rahmani, Marzieh Zamani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8093804/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Smart home research has expanded rapidly alongside advances in IoT, AI, and energy systems. We compile and deduplicate a 2000–2025 corpus of 3,020 records from Web of Science and Scopus and apply a transparent bibliometric workflow (bibliometrix/VOSviewer) to map publication dynamics, core outlets, collaboration networks, and conceptual clusters. Output accelerates after 2010 and concentrates in open-access, engineering-oriented journals. Co-occurrence analysis reveals four stable themes: (i) IoT infrastructure and ambient intelligence, (ii) energy management and smart grids, (iii) automation and intelligent building systems, and (iv) AI-based pattern recognition. We also document a shift from standalone automation to socio-technical, energy-aware ecosystems and outline practical implications around interoperability, privacy-by-design, and household-grid integration. All search strings, export settings, and cleaning rules are released for replication. Smart Homes Bibliometric Analysis IoT and Ambient Intelligence Energy Management and Smart Grids Sustainability and Digitalization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The ongoing digital transition is reshaping residential life, from device connectivity to data-driven services [ 1 ], [ 2 ]. The origins of the smart home concept can be traced back to early automation technologies [ 3 ], [ 4 ]. The development of the first home automation system in 1966 marked the initial step toward creating intelligent residential environments [ 5 ], [ 6 ]. Although conceptually related to “smart buildings” and “smart cities,” the scope of this paper is strictly residential. Accordingly, we use “smart home(s)” consistently and avoid conflating building-scale and city-scale constructs. In the home context, patterns of consumption, usage routines, and user preferences (comfort, thermal satisfaction, ease of use) are decisive [ 7 ], [ 8 ]. We define a smart home as a residence equipped with networked sensing and control that enables remote and autonomous operation of domestic services (safety, energy, health, comfort) [ 9 ], [ 10 ]. The primary objective of such systems is to enhance the comfort, safety, and overall well-being of occupants [ 11 ]. Smart homes harness scientific and technological innovations to deliver adaptive and efficient residential services that align with everyday human needs [ 12 ], [ 13 ]. These include energy management, elderly and healthcare support, home security, environmental control (such as ventilation and temperature regulation), as well as entertainment, communication, time management, and domestic tasks such as cooking and laundry [ 14 ]. Smart-home technologies can enhance lived experience while contributing to energy reduction and demand optimization, improving safety (e.g., gas/fire/ intrusion detection), enabling remote health monitoring, and empowering older adults to age in place [ 15 ], [ 16 ]. At the same time, the literature repeatedly highlights salient challenges: interoperability and standards misalignment, vendor lock-in, security vulnerabilities and privacy breaches, system reliability, and social acceptance, issues that call for integrative, interdisciplinary study [ 17 ], [ 18 ], [ 19 ]. Prior reviews are often narrative or stack-specific; few provide data-driven maps of temporal dynamics, collaboration, and concepts [ 20 ], [ 21 ], [ 22 ]. Although beneficial for enhancing subfields, such studies infrequently provide a data-driven comprehensive perspective on temporal dynamics, co-authorship frameworks, significant sources, and conceptual groupings. In this context, bibliometric analysis offers a quantitative, replicable approach to delineate the area, monitor theme progression over prolonged durations, and pinpoint significant contributors and pivotal contributions. The goal of this article is to provide a complete and replicable map of research on smart homes from 2000 to 2025. We address four questions: Q1: How has scientific output evolved (trends and milestones)? Q2: Who are the most influential authors, institutions, and countries (and how do they collaborate)? Q3: Which sources concentrate the field? Q4: Which conceptual clusters structure the domain? This paper aims to address the above research questions through a bibliometric analysis of the existing literature on smart home and smart home(s) technologies. Bibliometrics is a quantitative research method that applies statistical and network analysis techniques to scientific publications in order to evaluate research performance, map intellectual structures, and identify emerging trends within a specific field [ 23 ], [ 24 ]. By examining publication patterns, citation networks, keyword co-occurrences, and collaboration structures, bibliometric analysis provides a systematic and objective overview of the knowledge domain. In this study, it is employed to trace the evolution, thematic development, and research frontiers of smart home studies from 2000 to 2025. This approach enables the identification of influential authors, institutions, and countries, as well as the detection of conceptual clusters and shifting thematic priorities, thereby offering a comprehensive understanding of the field’s intellectual landscape and future research directions [ 25 ]. There are three contributions. First, a combined, deduplicated corpus is generated, which makes it possible to do multilayer network analysis (keyword co-occurrence, source co-citation, and geographic cooperation). Second, a thematic evolution map is made by taking time slices that show how the focus of home automation has changed over time to include IoT platforms, AAL, energy management, and security and privacy issues. Third, we break down the policy and research implications, such as interoperability and standardization, strong user-experience assessment, adding demand response to energy systems, and household-level data-governance frameworks. The paper proceeds as follows: Section 2 goes into further depth about the technique, such as the databases, search strategy, criteria for adding or excluding data, cleaning the data, and the analytical tools (bibliometrix/VOSviewer). In Section 3 , we talk about descriptive and network-based findings, such as trends in publications, key players, main sources, theme clusters, and the historiographic map. Section 4 presents conclusions and proposes a research program for the future. 2. Methodology A quantitative bibliometric mapping study was conducted to characterize the intellectual, thematic, and collaborative structure of research on smart homes in residential contexts. The corpus targeted publications between 2000 and 2025. Primary document types were articles and reviews; conference papers were analyzed in sensitivity checks (Section 2.11). English-language records formed the main corpus; Persian-language records were optionally included in robustness analyses when indexed. Records were retrieved from two multidisciplinary citation databases: Web of Science (WoS) Core Collection and Scopus. Search results from both databases were stored alongside exact query strings and export settings to enable reproducibility. Search strings were crafted to cover core expressions for smart homes while excluding adjacent but non-residential domains (e.g., smart hospitals, factories). Title/abstract/keyword fields were prioritized in Scopus; topic field (TS=) was used in WoS. The data collection process was conducted using two major academic databases, WoS and Scopus, covering the period from 2000 to 2025. The search strategy was designed to comprehensively capture literature related to smart homes and intelligent residential environments while excluding unrelated domains such as smart cities, factories, and hospitals. In Web of Science, the advanced search query included the following terms: “smart home,” “intelligent home*,” “domotic*,” “home automation,” “connected home*,” “ambient assisted living,”* and “AAL” combined with contextual terms such as “residential,” “smart apartment,” “household*,”* and “assisted living.” To refine the scope, records containing “smart hospital,” “smart factory*,”* or “smart city” near “infrastructure” were excluded. The search was limited to articles and review papers published in English or Persian. A parallel query was executed in Scopus using title, abstract, and keyword fields. The search expression mirrored the WoS query, employing the same inclusion and exclusion logic. Only documents categorized as articles (ar) or reviews (re) and published between 2000 and 2025 were retained. This dual-database strategy ensured comprehensive coverage and minimized the risk of omitting relevant studies in the domains of smart homes, home automation, and ambient assisted living. All query strings, date stamps, and export parameters are provided in the replication package. 3. Results 3.1 Publication trends A total of 3,020 research articles and reviews on smart homes were identified between 2000 and 2025, revealing a continuous upward trajectory in annual scientific production. As shown in Fig. 1 , the number of publications remained marginal until 2010, followed by a sharp increase during 2013–2015, coinciding with the rapid diffusion of Internet of Things (IoT) technologies and the introduction of intelligent sensors in domestic environments. Publication output peaked in 2024 with approximately 300 documents, marking a fivefold increase compared with 2015 levels. A slight decline observed in 2025 (~ 240 articles) can be attributed to incomplete indexation for the current year. This growth pattern demonstrates that smart home research has evolved from a niche experimental topic into a consolidated, multidisciplinary domain spanning engineering, information technology, and energy management. Moreover, the disciplinary profile of smart home research (Fig. 2 ) demonstrates a strong technological orientation complemented by increasing interdisciplinary engagement. Computer Science (25.0%) and Engineering (24.6%) together account for roughly half of all indexed documents, underscoring the field’s firm foundation in software design, embedded systems, and networked automation. These domains have traditionally driven advancements in home automation architectures, communication protocols, and intelligent control systems. Secondary but significant contributions arise from Energy (7.0%), Medicine (5.3%), Physics and Astronomy (5.2%), and Mathematics (4.9%), reflecting the expansion of smart home applications toward energy optimization, health monitoring, sensor physics, and computational modeling. Emerging participation from the Social Sciences (4.6%), Biochemistry, Genetics and Molecular Biology (3.9%), Chemistry (3.3%), and Materials Science (3.0%) highlights the growing convergence between technology, human factors, and material innovation. The “Other” (13.1%) category encompasses a wide spectrum of fields, including business, environmental sciences, and health professions, illustrating the diffusion of smart home technologies into broader socioeconomic and sustainability contexts. Collectively, this disciplinary landscape reveals that research on smart homes has evolved into a mature multidisciplinary domain, integrating technical development with human-centered, medical, and environmental perspectives. 3.2 Core publication sources Figure 3 depicts the evolution of leading journals and conference proceedings contributing to the field. Sensors (MDPI) consistently ranks as the top outlet, with pronounced peaks in 2014 and 2019 that correspond to waves of IoT system deployment. IEEE Access emerged as a strong multidisciplinary platform around 2018–2021, while Energies exhibited steady annual growth reflecting the integration of smart home technologies into energy efficiency and sustainability research. The Journal of Ambient Intelligence and Smart Environments and Future Generation Computer Systems provided earlier theoretical and systems-oriented contributions. The results confirm a transition from specialized niche outlets to high-visibility, open-access venues emphasizing applied IoT, energy systems, and human-centered innovation. 3.3 Leading institutions and geographical distribution Institutional productivity is geographically diverse yet dominated by European research networks (Fig. 4 ). The Consiglio Nazionale delle Ricerche (CNR) leads with 53 publications, followed by Università di Parma (41) and Università Politecnica delle Marche (35). Outside Europe, King Saud University (Saudi Arabia), Ulster University (UK), and Canadian universities such as Toronto and Waterloo each contribute over 30 papers. Universidade do Minho (Portugal) and Aalborg University (Denmark) round out the top group, underscoring the field’s collaborative and international orientation. These institutions serve as focal points for research on home automation, assisted living, and energy-aware smart environments. To assess the global structure of research collaboration in the smart home(s) domain, an international co-authorship analysis was conducted using country-level affiliations. Each node in Fig. 5 represents a country, with node size proportional to its publication output and link thickness corresponding to the strength of co-authorship ties. The resulting network reveals a densely interconnected global structure, dominated by several regional collaboration hubs. The United States, China, and the United Kingdom form the central triad, acting as key mediators between Asian and Western research communities. China demonstrates the highest publication volume and extensive partnerships with India, Malaysia, South Korea, and Iran, highlighting its growing leadership in IoT and energy-focused smart home technologies. European countries form multiple cohesive clusters, particularly Italy, Spain, the United Kingdom, France, and Germany, characterized by strong intra-European cooperation and links to developing regions. Spain, notably, emerges as a highly connected node bridging Europe with Latin America, particularly Mexico and Colombia. Similarly, Italy and Greece anchor the Mediterranean cluster with frequent collaboration in energy efficiency and building automation research. Countries such as Australia, Canada, and South Korea also exhibit high centrality, serving as secondary hubs facilitating transcontinental knowledge exchange. Meanwhile, emerging contributions from Iran, Pakistan, and Egypt indicate increasing participation from developing economies in smart technology and sustainability research. The map demonstrates a globalized yet regionally cohesive collaboration pattern, where advanced economies drive research intensity while developing nations contribute to expanding the field’s diversity and regional relevance. This structure reflects the interdisciplinary and international nature of smart home(s) research, combining engineering, information technology, and environmental science within a collaborative framework. 3.4. Conceptual Structure of the Field To uncover the intellectual and thematic organization of smart home research, a keyword co-occurrence analysis was conducted using author keywords from the 3,020-document corpus. A minimum frequency threshold was applied to include only the most relevant and interconnected concepts. The resulting network (Fig. 6 ) reveals a highly interconnected structure comprising four dominant thematic clusters that together capture the evolution and diversity of the field. Cluster 1 – IoT Infrastructure and Ambient Intelligence (Red Cluster) This cluster represents the technological foundation of smart home research. It is dominated by terms such as Internet of Things , ambient intelligence , machine learning , deep learning , wearable sensors , assistive technology , and quality of life . These keywords reflect the convergence of IoT-based architectures with artificial intelligence to enhance automation, perception, and adaptability within the home environment. The strong associations between IoT , machine learning , and artificial intelligence suggest that smart homes are increasingly conceptualized as cognitive ecosystems, capable of data-driven sensing and autonomous decision-making. The frequent appearance of healthcare , elderly care , and assistive technology underscores the growing importance of human-centered applications, particularly in aging populations and personalized healthcare contexts. Subthemes such as wireless communication , edge computing , and cloud computing indicate technological efforts to improve scalability, responsiveness, and data processing efficiency. Cluster 2 – Energy Management, Smart Grids, and Sustainability (Green Cluster) The second cluster captures the energy and environmental dimension of smart home research. Core terms include energy management , optimization , demand response , smart grid , renewable energy , energy efficiency , electric vehicles , and sustainable development . This thematic stream reflects the integration of smart homes into broader smart energy ecosystems, where buildings act as active nodes in demand–supply regulation, energy storage, and renewable integration. The frequent co-occurrence of energy management with optimization and demand response highlights the centrality of energy-saving strategies, while links to renewable energy and climate change indicate alignment with global sustainability goals. Recent works focus on intelligent control systems for dynamic load forecasting, battery management, and energy pricing optimization—showing the progression from local automation toward system-level energy orchestration. Cluster 3 – Automation and Intelligent Building Systems (Blue Cluster) The blue cluster represents the architectural and systems-integration core of the field, connecting the technical and managerial aspects of smart homes. Central terms such as automation , intelligent buildings , simulation , energy conservation , and decision-making define this stream. It emphasizes research on integrating multiple subsystems, lighting, HVAC, and appliances, through automated control architectures that balance comfort, efficiency, and sustainability. Moreover, terms like economic and social effects and sustainable development suggest that research has moved beyond the technical dimension to include socioeconomic implications and policy relevance. The strong connectivity between this cluster and others (notably energy management and IoT systems) positions it as the conceptual backbone of the entire field, bridging operational control with environmental and human considerations. Cluster 4 – Artificial Intelligence and Pattern Recognition (Yellow-Orange Substream) Although smaller in size, this cluster has significant methodological influence. It includes terms such as pattern recognition , deep learning , learning algorithms , and data mining , which highlight the adoption of data-centric and AI-driven approaches. This cluster underpins the predictive and adaptive capabilities of smart home systems, enabling functions like activity recognition, anomaly detection, energy forecasting, and personalized automation. The interlinkages with both IoT and automation clusters demonstrate how AI serves as a cross-cutting technological enabler for the entire ecosystem. To identify the most dynamic and fast-evolving topics in the smart home(s) domain, a citation burst analysis was performed on the author keywords extracted from the 3,020-document corpus. This approach detects periods in which specific terms experienced a sudden increase in citations, signaling heightened scholarly attention or the emergence of new paradigms. As shown in Fig. 7 , the temporal evolution of citation bursts reveals a clear transition from early automation technologies to contemporary intelligent and energy-aware systems. The initial research phase (2000–2015) was dominated by keywords such as home automation and ambient assisted living, reflecting the foundations of domestic automation and assistive technologies for elderly care. Between 2013 and 2018, the emergence of Internet of Things (IoT), smart grid, and energy management marked the integration of connectivity and sustainability concerns into building systems. This period coincides with the widespread adoption of sensor networks, data-driven control, and the linkage of homes to larger smart energy ecosystems. From 2018 onward, a new wave of research frontiers has emerged, characterized by artificial intelligence (AI), machine learning, deep learning, and data analytics. These terms indicate the shift toward predictive modeling, adaptive control, and context-aware automation. Simultaneously, IoT security, edge computing, and blockchain represent critical advancements in ensuring data integrity, privacy, and decentralized operation within interconnected infrastructures. The citation burst analysis confirms a progressive paradigm shift: from standalone automation and monitoring systems to intelligent, secure, and sustainable smart home(s) ecosystems that integrate AI, energy optimization, and digital trust frameworks. 4. Conclusion This bibliometric study provides a comprehensive mapping of the smart home research landscape from 2000 to 2025, based on a curated and deduplicated corpus of 3,020 scientific documents drawn primarily from the Web of Science and Scopus databases. Through a combination of descriptive, source-based, and network-based analyses, the evolution, thematic composition, and institutional structure of this rapidly expanding field were revealed. The longitudinal trend analysis demonstrated a sharp and sustained increase in scholarly output since 2010, coinciding with the technological diffusion of the Internet of Things (IoT), affordable sensors, and cloud-based services. Core publication sources such as Sensors , IEEE Access , and Energies have served as the principal dissemination channels, reflecting the transition from experimental prototypes to multidisciplinary and applied studies. The most active institutions, predominantly European and Asian, illustrate the globalized and collaborative character of the field, with Italian and Saudi universities among the leading contributors. The subject-area classification confirmed that smart home research is dominated by Computer Science and Engineering, but has increasingly incorporated Energy, Social Sciences, and Medicine, evidencing a move toward socio-technical integration. The keyword co-occurrence network further exposed four interlinked conceptual clusters: IoT infrastructure and ambient intelligence, highlighting AI-enabled sensing and automation; Energy management and smart grids, emphasizing optimization and sustainability; Automation and intelligent buildings, integrating efficiency and user comfort; and Artificial intelligence and pattern recognition, serving as methodological drivers across the domain. Together, these findings portray a field that has evolved from isolated home automation systems into an interdisciplinary ecosystem where technology, energy, and human well-being converge. The trajectory of research suggests a continuing shift toward data-driven, energy-aware, and user-adaptive environments supported by interoperable IoT frameworks and intelligent control architectures. From a research-policy perspective, the results underscore the need for: Standardization and interoperability protocols to enable cross-platform integration; User-centric design and privacy governance to ensure trust and adoption; Holistic energy frameworks linking households to smart grids and demand-response systems; and Ethical and sustainability assessments of AI-enabled domestic technologies. This paper affirms that smart home research has matured into a strategic pillar of the digital and sustainable transition, bridging computer engineering, energy science, and human factors. Future work should expand the bibliometric horizon through full-text mining, citation context analysis, and regional comparative mapping to better capture emerging paradigms such as AI-driven autonomy, edge computing, and green digital infrastructures in next-generation intelligent living environments. Declarations Funding: This research received no external funding. Competing interests: The authors declare that they have no competing interests. Ethics approval and consent to participate: Not applicable. This study is based exclusively on bibliometric analysis of published literature and does not involve human participants, animals, or personally identifiable data. Consent for publication: Not applicable. Availability of data and materials: The bibliometric dataset (WoS and Scopus exports), search strings, and data-cleaning scripts used in this study are available from the corresponding author on reasonable request. Author contributions: A.R. and M.Z. jointly conceived the study and designed the bibliometric methodology. A.R. collected and cleaned the data and performed the formal analysis. M.Z. contributed to interpretation of the results and the development of the conceptual framing. Both authors contributed to writing the original draft and to revising and editing the manuscript. Both authors have read and approved the final version of the manuscript. References Lee S, Choi D-H. Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources. IEEE Trans Ind Inf. 2020;18(1):488–97. Chatrati SP, et al. Smart home health monitoring system for predicting type 2 diabetes and hypertension. J King Saud Univ Inf Sci. 2022;34(3):862–70. Kulurkar P, kumar Dixit C, Bharathi VC, Monikavishnuvarthini A, Dhakne A, Preethi P. AI based elderly fall prediction system using wearable sensors: A smart home-care technology with IOT. Meas Sens. 2023;25:100614. 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15:37:09","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23768,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/2de25a65af7a58240cc7f399.png"},{"id":97769439,"identity":"241bac27-1ba1-4c40-bb8c-ffb2079dbeea","added_by":"auto","created_at":"2025-12-09 07:45:11","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69937,"visible":true,"origin":"","legend":"","description":"","filename":"64e88b050a1449aa804fa47777e266aa1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/07670c6671356135cbc60ae3.xml"},{"id":97895343,"identity":"19f12fc6-769d-4dd9-80af-4207ea60b8b6","added_by":"auto","created_at":"2025-12-10 15:34:01","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77424,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/22078a0c8b523e7fd2ea40d9.html"},{"id":97769421,"identity":"0d73c88d-4170-4a85-aaa1-b4d6f1251519","added_by":"auto","created_at":"2025-12-09 07:45:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAnnual scientific production of smart home research, 2000–2025 (N = 3,020).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/4b59aedff946d76f543faba8.png"},{"id":97769423,"identity":"6d5b2dca-499b-4771-adbc-5387372f09e3","added_by":"auto","created_at":"2025-12-09 07:45:11","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of smart home publications by subject area (2000–2025).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/083320cc3fbab590469d409f.jpeg"},{"id":97896910,"identity":"5c3a38b9-4dbe-4870-9638-802521bb6025","added_by":"auto","created_at":"2025-12-10 15:37:11","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":384577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eYearly output of top publication sources.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/8ef5b6d69243cba54ea18e65.jpeg"},{"id":97769422,"identity":"fd2fccc5-ef17-456e-8105-e01c7f58515a","added_by":"auto","created_at":"2025-12-09 07:45:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTop contributing institutions in smart home research.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/fbe18d3d3b629e2a61396d62.png"},{"id":97897430,"identity":"c8822dda-83da-4d87-8d8c-2bc62f33fac5","added_by":"auto","created_at":"2025-12-10 15:37:49","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1667064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInternational co-authorship network in smart home(s) research (2000–2025). Node size represents publication volume; link thickness indicates co-authorship strength; and colors denote collaboration clusters.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/4a0aa2f5a15c51ec36664bb5.png"},{"id":97897490,"identity":"c22fc08b-d0da-48e5-97b4-4635b510d0f5","added_by":"auto","created_at":"2025-12-10 15:37:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2527553,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKeyword co-occurrence network visualized using VOSviewer. Node size represents keyword frequency, link thickness reflects co-occurrence strength, and colors denote thematic clusters.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/d90f96de8b10790ef03169cc.png"},{"id":97769426,"identity":"df323cca-7cb2-4e17-b24e-27ad5131fa89","added_by":"auto","created_at":"2025-12-09 07:45:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":95321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKeywords with the strongest citation bursts in smart home(s) research (2000–2025). Red bars indicate active burst periods; turquoise lines represent the total timeline.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/8d9f12c4ae93befed14d3283.png"},{"id":101753920,"identity":"a4fb6f0c-109b-40cd-9604-ad403ae7641c","added_by":"auto","created_at":"2026-02-03 10:41:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6003311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8093804/v1/9f78eb5a-0c43-427a-ba22-8f38fde02146.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Smart homes in the Era of Sustainability and Digitalization: A Bibliometric Review","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe ongoing digital transition is reshaping residential life, from device connectivity to data-driven services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The origins of the smart home concept can be traced back to early automation technologies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The development of the first home automation system in 1966 marked the initial step toward creating intelligent residential environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although conceptually related to \u0026ldquo;smart buildings\u0026rdquo; and \u0026ldquo;smart cities,\u0026rdquo; the scope of this paper is strictly residential. Accordingly, we use \u0026ldquo;smart home(s)\u0026rdquo; consistently and avoid conflating building-scale and city-scale constructs. In the home context, patterns of consumption, usage routines, and user preferences (comfort, thermal satisfaction, ease of use) are decisive [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We define a smart home as a residence equipped with networked sensing and control that enables remote and autonomous operation of domestic services (safety, energy, health, comfort) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The primary objective of such systems is to enhance the comfort, safety, and overall well-being of occupants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Smart homes harness scientific and technological innovations to deliver adaptive and efficient residential services that align with everyday human needs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These include energy management, elderly and healthcare support, home security, environmental control (such as ventilation and temperature regulation), as well as entertainment, communication, time management, and domestic tasks such as cooking and laundry [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSmart-home technologies can enhance lived experience while contributing to energy reduction and demand optimization, improving safety (e.g., gas/fire/ intrusion detection), enabling remote health monitoring, and empowering older adults to age in place [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. At the same time, the literature repeatedly highlights salient challenges: interoperability and standards misalignment, vendor lock-in, security vulnerabilities and privacy breaches, system reliability, and social acceptance, issues that call for integrative, interdisciplinary study [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrior reviews are often narrative or stack-specific; few provide data-driven maps of temporal dynamics, collaboration, and concepts [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although beneficial for enhancing subfields, such studies infrequently provide a data-driven comprehensive perspective on temporal dynamics, co-authorship frameworks, significant sources, and conceptual groupings. In this context, bibliometric analysis offers a quantitative, replicable approach to delineate the area, monitor theme progression over prolonged durations, and pinpoint significant contributors and pivotal contributions.\u003c/p\u003e\u003cp\u003eThe goal of this article is to provide a complete and replicable map of research on smart homes from 2000 to 2025. We address four questions: Q1: How has scientific output evolved (trends and milestones)? Q2: Who are the most influential authors, institutions, and countries (and how do they collaborate)? Q3: Which sources concentrate the field? Q4: Which conceptual clusters structure the domain?\u003c/p\u003e\u003cp\u003eThis paper aims to address the above research questions through a bibliometric analysis of the existing literature on smart home and smart home(s) technologies. Bibliometrics is a quantitative research method that applies statistical and network analysis techniques to scientific publications in order to evaluate research performance, map intellectual structures, and identify emerging trends within a specific field [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By examining publication patterns, citation networks, keyword co-occurrences, and collaboration structures, bibliometric analysis provides a systematic and objective overview of the knowledge domain. In this study, it is employed to trace the evolution, thematic development, and research frontiers of smart home studies from 2000 to 2025. This approach enables the identification of influential authors, institutions, and countries, as well as the detection of conceptual clusters and shifting thematic priorities, thereby offering a comprehensive understanding of the field\u0026rsquo;s intellectual landscape and future research directions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThere are three contributions. First, a combined, deduplicated corpus is generated, which makes it possible to do multilayer network analysis (keyword co-occurrence, source co-citation, and geographic cooperation). Second, a thematic evolution map is made by taking time slices that show how the focus of home automation has changed over time to include IoT platforms, AAL, energy management, and security and privacy issues. Third, we break down the policy and research implications, such as interoperability and standardization, strong user-experience assessment, adding demand response to energy systems, and household-level data-governance frameworks.\u003c/p\u003e\u003cp\u003eThe paper proceeds as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e goes into further depth about the technique, such as the databases, search strategy, criteria for adding or excluding data, cleaning the data, and the analytical tools (bibliometrix/VOSviewer). In Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we talk about descriptive and network-based findings, such as trends in publications, key players, main sources, theme clusters, and the historiographic map. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents conclusions and proposes a research program for the future.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eA quantitative bibliometric mapping study was conducted to characterize the intellectual, thematic, and collaborative structure of research on smart homes in residential contexts. The corpus targeted publications between 2000 and 2025. Primary document types were articles and reviews; conference papers were analyzed in sensitivity checks (Section 2.11). English-language records formed the main corpus; Persian-language records were optionally included in robustness analyses when indexed. Records were retrieved from two multidisciplinary citation databases: Web of Science (WoS) Core Collection and Scopus. Search results from both databases were stored alongside exact query strings and export settings to enable reproducibility.\u003c/p\u003e\u003cp\u003eSearch strings were crafted to cover core expressions for smart homes while excluding adjacent but non-residential domains (e.g., smart hospitals, factories). Title/abstract/keyword fields were prioritized in Scopus; topic field (TS=) was used in WoS. The data collection process was conducted using two major academic databases, WoS and Scopus, covering the period from 2000 to 2025. The search strategy was designed to comprehensively capture literature related to smart homes and intelligent residential environments while excluding unrelated domains such as smart cities, factories, and hospitals. In Web of Science, the advanced search query included the following terms: \u0026ldquo;smart home,\u0026rdquo; \u0026ldquo;intelligent home*,\u0026rdquo; \u0026ldquo;domotic*,\u0026rdquo; \u0026ldquo;home automation,\u0026rdquo; \u0026ldquo;connected home*,\u0026rdquo; \u0026ldquo;ambient assisted living,\u0026rdquo;* and \u0026ldquo;AAL\u0026rdquo; combined with contextual terms such as \u0026ldquo;residential,\u0026rdquo; \u0026ldquo;smart apartment,\u0026rdquo; \u0026ldquo;household*,\u0026rdquo;* and \u0026ldquo;assisted living.\u0026rdquo; To refine the scope, records containing \u0026ldquo;smart hospital,\u0026rdquo; \u0026ldquo;smart factory*,\u0026rdquo;* or \u0026ldquo;smart city\u0026rdquo; near \u0026ldquo;infrastructure\u0026rdquo; were excluded. The search was limited to articles and review papers published in English or Persian.\u003c/p\u003e\u003cp\u003eA parallel query was executed in Scopus using title, abstract, and keyword fields. The search expression mirrored the WoS query, employing the same inclusion and exclusion logic. Only documents categorized as articles (ar) or reviews (re) and published between 2000 and 2025 were retained. This dual-database strategy ensured comprehensive coverage and minimized the risk of omitting relevant studies in the domains of smart homes, home automation, and ambient assisted living. All query strings, date stamps, and export parameters are provided in the replication package.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Publication trends\u003c/h2\u003e\u003cp\u003eA total of 3,020 research articles and reviews on smart homes were identified between 2000 and 2025, revealing a continuous upward trajectory in annual scientific production. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the number of publications remained marginal until 2010, followed by a sharp increase during 2013\u0026ndash;2015, coinciding with the rapid diffusion of Internet of Things (IoT) technologies and the introduction of intelligent sensors in domestic environments.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePublication output peaked in 2024 with approximately 300 documents, marking a fivefold increase compared with 2015 levels. A slight decline observed in 2025 (~\u0026thinsp;240 articles) can be attributed to incomplete indexation for the current year. This growth pattern demonstrates that smart home research has evolved from a niche experimental topic into a consolidated, multidisciplinary domain spanning engineering, information technology, and energy management.\u003c/p\u003e\u003cp\u003eMoreover, the disciplinary profile of smart home research (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) demonstrates a strong technological orientation complemented by increasing interdisciplinary engagement.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComputer Science (25.0%) and Engineering (24.6%) together account for roughly half of all indexed documents, underscoring the field\u0026rsquo;s firm foundation in software design, embedded systems, and networked automation. These domains have traditionally driven advancements in home automation architectures, communication protocols, and intelligent control systems. Secondary but significant contributions arise from Energy (7.0%), Medicine (5.3%), Physics and Astronomy (5.2%), and Mathematics (4.9%), reflecting the expansion of smart home applications toward energy optimization, health monitoring, sensor physics, and computational modeling. Emerging participation from the Social Sciences (4.6%), Biochemistry, Genetics and Molecular Biology (3.9%), Chemistry (3.3%), and Materials Science (3.0%) highlights the growing convergence between technology, human factors, and material innovation. The \u0026ldquo;Other\u0026rdquo; (13.1%) category encompasses a wide spectrum of fields, including business, environmental sciences, and health professions, illustrating the diffusion of smart home technologies into broader socioeconomic and sustainability contexts. Collectively, this disciplinary landscape reveals that research on smart homes has evolved into a mature multidisciplinary domain, integrating technical development with human-centered, medical, and environmental perspectives.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Core publication sources\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the evolution of leading journals and conference proceedings contributing to the field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSensors (MDPI) consistently ranks as the top outlet, with pronounced peaks in 2014 and 2019 that correspond to waves of IoT system deployment. IEEE Access emerged as a strong multidisciplinary platform around 2018\u0026ndash;2021, while Energies exhibited steady annual growth reflecting the integration of smart home technologies into energy efficiency and sustainability research. The Journal of Ambient Intelligence and Smart Environments and Future Generation Computer Systems provided earlier theoretical and systems-oriented contributions. The results confirm a transition from specialized niche outlets to high-visibility, open-access venues emphasizing applied IoT, energy systems, and human-centered innovation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Leading institutions and geographical distribution\u003c/h2\u003e\u003cp\u003eInstitutional productivity is geographically diverse yet dominated by European research networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Consiglio Nazionale delle Ricerche (CNR) leads with 53 publications, followed by Universit\u0026agrave; di Parma (41) and Universit\u0026agrave; Politecnica delle Marche (35). Outside Europe, King Saud University (Saudi Arabia), Ulster University (UK), and Canadian universities such as Toronto and Waterloo each contribute over 30 papers. Universidade do Minho (Portugal) and Aalborg University (Denmark) round out the top group, underscoring the field\u0026rsquo;s collaborative and international orientation. These institutions serve as focal points for research on home automation, assisted living, and energy-aware smart environments.\u003c/p\u003e\u003cp\u003eTo assess the global structure of research collaboration in the smart home(s) domain, an international co-authorship analysis was conducted using country-level affiliations. Each node in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e represents a country, with node size proportional to its publication output and link thickness corresponding to the strength of co-authorship ties.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe resulting network reveals a densely interconnected global structure, dominated by several regional collaboration hubs. The United States, China, and the United Kingdom form the central triad, acting as key mediators between Asian and Western research communities. China demonstrates the highest publication volume and extensive partnerships with India, Malaysia, South Korea, and Iran, highlighting its growing leadership in IoT and energy-focused smart home technologies. European countries form multiple cohesive clusters, particularly Italy, Spain, the United Kingdom, France, and Germany, characterized by strong intra-European cooperation and links to developing regions. Spain, notably, emerges as a highly connected node bridging Europe with Latin America, particularly Mexico and Colombia. Similarly, Italy and Greece anchor the Mediterranean cluster with frequent collaboration in energy efficiency and building automation research. Countries such as Australia, Canada, and South Korea also exhibit high centrality, serving as secondary hubs facilitating transcontinental knowledge exchange. Meanwhile, emerging contributions from Iran, Pakistan, and Egypt indicate increasing participation from developing economies in smart technology and sustainability research. The map demonstrates a globalized yet regionally cohesive collaboration pattern, where advanced economies drive research intensity while developing nations contribute to expanding the field\u0026rsquo;s diversity and regional relevance. This structure reflects the interdisciplinary and international nature of smart home(s) research, combining engineering, information technology, and environmental science within a collaborative framework.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Conceptual Structure of the Field\u003c/h2\u003e\u003cp\u003eTo uncover the intellectual and thematic organization of smart home research, a keyword co-occurrence analysis was conducted using author keywords from the 3,020-document corpus. A minimum frequency threshold was applied to include only the most relevant and interconnected concepts. The resulting network (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) reveals a highly interconnected structure comprising four dominant thematic clusters that together capture the evolution and diversity of the field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCluster 1 \u0026ndash; IoT Infrastructure and Ambient Intelligence (Red Cluster)\u003c/p\u003e\u003cp\u003eThis cluster represents the technological foundation of smart home research. It is dominated by terms such as \u003cem\u003eInternet of Things\u003c/em\u003e, \u003cem\u003eambient intelligence\u003c/em\u003e, \u003cem\u003emachine learning\u003c/em\u003e, \u003cem\u003edeep learning\u003c/em\u003e, \u003cem\u003ewearable sensors\u003c/em\u003e, \u003cem\u003eassistive technology\u003c/em\u003e, and \u003cem\u003equality of life\u003c/em\u003e. These keywords reflect the convergence of IoT-based architectures with artificial intelligence to enhance automation, perception, and adaptability within the home environment.\u003c/p\u003e\u003cp\u003eThe strong associations between \u003cem\u003eIoT\u003c/em\u003e, \u003cem\u003emachine learning\u003c/em\u003e, and \u003cem\u003eartificial intelligence\u003c/em\u003e suggest that smart homes are increasingly conceptualized as cognitive ecosystems, capable of data-driven sensing and autonomous decision-making. The frequent appearance of \u003cem\u003ehealthcare\u003c/em\u003e, \u003cem\u003eelderly care\u003c/em\u003e, and \u003cem\u003eassistive technology\u003c/em\u003e underscores the growing importance of human-centered applications, particularly in aging populations and personalized healthcare contexts. Subthemes such as \u003cem\u003ewireless communication\u003c/em\u003e, \u003cem\u003eedge computing\u003c/em\u003e, and \u003cem\u003ecloud computing\u003c/em\u003e indicate technological efforts to improve scalability, responsiveness, and data processing efficiency.\u003c/p\u003e\u003cp\u003eCluster 2 \u0026ndash; Energy Management, Smart Grids, and Sustainability (Green Cluster)\u003c/p\u003e\u003cp\u003eThe second cluster captures the energy and environmental dimension of smart home research. Core terms include \u003cem\u003eenergy management\u003c/em\u003e, \u003cem\u003eoptimization\u003c/em\u003e, \u003cem\u003edemand response\u003c/em\u003e, \u003cem\u003esmart grid\u003c/em\u003e, \u003cem\u003erenewable energy\u003c/em\u003e, \u003cem\u003eenergy efficiency\u003c/em\u003e, \u003cem\u003eelectric vehicles\u003c/em\u003e, and \u003cem\u003esustainable development\u003c/em\u003e. This thematic stream reflects the integration of smart homes into broader smart energy ecosystems, where buildings act as active nodes in demand\u0026ndash;supply regulation, energy storage, and renewable integration.\u003c/p\u003e\u003cp\u003eThe frequent co-occurrence of \u003cem\u003eenergy management\u003c/em\u003e with \u003cem\u003eoptimization\u003c/em\u003e and \u003cem\u003edemand response\u003c/em\u003e highlights the centrality of energy-saving strategies, while links to \u003cem\u003erenewable energy\u003c/em\u003e and \u003cem\u003eclimate change\u003c/em\u003e indicate alignment with global sustainability goals. Recent works focus on intelligent control systems for dynamic load forecasting, battery management, and energy pricing optimization\u0026mdash;showing the progression from local automation toward system-level energy orchestration.\u003c/p\u003e\u003cp\u003eCluster 3 \u0026ndash; Automation and Intelligent Building Systems (Blue Cluster)\u003c/p\u003e\u003cp\u003eThe blue cluster represents the architectural and systems-integration core of the field, connecting the technical and managerial aspects of smart homes. Central terms such as \u003cem\u003eautomation\u003c/em\u003e, \u003cem\u003eintelligent buildings\u003c/em\u003e, \u003cem\u003esimulation\u003c/em\u003e, \u003cem\u003eenergy conservation\u003c/em\u003e, and \u003cem\u003edecision-making\u003c/em\u003e define this stream. It emphasizes research on integrating multiple subsystems, lighting, HVAC, and appliances, through automated control architectures that balance comfort, efficiency, and sustainability.\u003c/p\u003e\u003cp\u003eMoreover, terms like \u003cem\u003eeconomic and social effects\u003c/em\u003e and \u003cem\u003esustainable development\u003c/em\u003e suggest that research has moved beyond the technical dimension to include socioeconomic implications and policy relevance. The strong connectivity between this cluster and others (notably energy management and IoT systems) positions it as the conceptual backbone of the entire field, bridging operational control with environmental and human considerations.\u003c/p\u003e\u003cp\u003eCluster 4 \u0026ndash; Artificial Intelligence and Pattern Recognition (Yellow-Orange Substream)\u003c/p\u003e\u003cp\u003eAlthough smaller in size, this cluster has significant methodological influence. It includes terms such as \u003cem\u003epattern recognition\u003c/em\u003e, \u003cem\u003edeep learning\u003c/em\u003e, \u003cem\u003elearning algorithms\u003c/em\u003e, and \u003cem\u003edata mining\u003c/em\u003e, which highlight the adoption of data-centric and AI-driven approaches.\u003c/p\u003e\u003cp\u003eThis cluster underpins the predictive and adaptive capabilities of smart home systems, enabling functions like activity recognition, anomaly detection, energy forecasting, and personalized automation. The interlinkages with both IoT and automation clusters demonstrate how AI serves as a cross-cutting technological enabler for the entire ecosystem.\u003c/p\u003e\u003cp\u003eTo identify the most dynamic and fast-evolving topics in the smart home(s) domain, a citation burst analysis was performed on the author keywords extracted from the 3,020-document corpus. This approach detects periods in which specific terms experienced a sudden increase in citations, signaling heightened scholarly attention or the emergence of new paradigms. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the temporal evolution of citation bursts reveals a clear transition from early automation technologies to contemporary intelligent and energy-aware systems.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe initial research phase (2000\u0026ndash;2015) was dominated by keywords such as home automation and ambient assisted living, reflecting the foundations of domestic automation and assistive technologies for elderly care. Between 2013 and 2018, the emergence of Internet of Things (IoT), smart grid, and energy management marked the integration of connectivity and sustainability concerns into building systems. This period coincides with the widespread adoption of sensor networks, data-driven control, and the linkage of homes to larger smart energy ecosystems. From 2018 onward, a new wave of research frontiers has emerged, characterized by artificial intelligence (AI), machine learning, deep learning, and data analytics. These terms indicate the shift toward predictive modeling, adaptive control, and context-aware automation. Simultaneously, IoT security, edge computing, and blockchain represent critical advancements in ensuring data integrity, privacy, and decentralized operation within interconnected infrastructures. The citation burst analysis confirms a progressive paradigm shift: from standalone automation and monitoring systems to intelligent, secure, and sustainable smart home(s) ecosystems that integrate AI, energy optimization, and digital trust frameworks.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis bibliometric study provides a comprehensive mapping of the smart home research landscape from 2000 to 2025, based on a curated and deduplicated corpus of 3,020 scientific documents drawn primarily from the Web of Science and Scopus databases. Through a combination of descriptive, source-based, and network-based analyses, the evolution, thematic composition, and institutional structure of this rapidly expanding field were revealed.\u003c/p\u003e\u003cp\u003eThe longitudinal trend analysis demonstrated a sharp and sustained increase in scholarly output since 2010, coinciding with the technological diffusion of the Internet of Things (IoT), affordable sensors, and cloud-based services. Core publication sources such as \u003cem\u003eSensors\u003c/em\u003e, \u003cem\u003eIEEE Access\u003c/em\u003e, and \u003cem\u003eEnergies\u003c/em\u003e have served as the principal dissemination channels, reflecting the transition from experimental prototypes to multidisciplinary and applied studies. The most active institutions, predominantly European and Asian, illustrate the globalized and collaborative character of the field, with Italian and Saudi universities among the leading contributors.\u003c/p\u003e\u003cp\u003eThe subject-area classification confirmed that smart home research is dominated by Computer Science and Engineering, but has increasingly incorporated Energy, Social Sciences, and Medicine, evidencing a move toward socio-technical integration. The keyword co-occurrence network further exposed four interlinked conceptual clusters:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIoT infrastructure and ambient intelligence, highlighting AI-enabled sensing and automation;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnergy management and smart grids, emphasizing optimization and sustainability;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAutomation and intelligent buildings, integrating efficiency and user comfort; and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eArtificial intelligence and pattern recognition, serving as methodological drivers across the domain.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTogether, these findings portray a field that has evolved from isolated home automation systems into an interdisciplinary ecosystem where technology, energy, and human well-being converge. The trajectory of research suggests a continuing shift toward data-driven, energy-aware, and user-adaptive environments supported by interoperable IoT frameworks and intelligent control architectures.\u003c/p\u003e\u003cp\u003eFrom a research-policy perspective, the results underscore the need for:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eStandardization and interoperability protocols to enable cross-platform integration;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUser-centric design and privacy governance to ensure trust and adoption;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHolistic energy frameworks linking households to smart grids and demand-response systems; and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEthical and sustainability assessments of AI-enabled domestic technologies.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis paper affirms that smart home research has matured into a strategic pillar of the digital and sustainable transition, bridging computer engineering, energy science, and human factors. Future work should expand the bibliometric horizon through full-text mining, citation context analysis, and regional comparative mapping to better capture emerging paradigms such as AI-driven autonomy, edge computing, and green digital infrastructures in next-generation intelligent living environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e Not applicable. This study is based exclusively on bibliometric analysis of published literature and does not involve human participants, animals, or personally identifiable data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e The bibliometric dataset (WoS and Scopus exports), search strings, and data-cleaning scripts used in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.R. and M.Z. jointly conceived the study and designed the bibliometric methodology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA.R. collected and cleaned the data and performed the formal analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eM.Z. contributed to interpretation of the results and the development of the conceptual framing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth authors contributed to writing the original draft and to revising and editing the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBoth authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLee S, Choi D-H. 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Mar Drugs. 2025;23(1):34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Smart Homes, Bibliometric Analysis, IoT and Ambient Intelligence, Energy Management and Smart Grids, Sustainability and Digitalization","lastPublishedDoi":"10.21203/rs.3.rs-8093804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8093804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmart home research has expanded rapidly alongside advances in IoT, AI, and energy systems. We compile and deduplicate a 2000\u0026ndash;2025 corpus of 3,020 records from Web of Science and Scopus and apply a transparent bibliometric workflow (bibliometrix/VOSviewer) to map publication dynamics, core outlets, collaboration networks, and conceptual clusters. Output accelerates after 2010 and concentrates in open-access, engineering-oriented journals. Co-occurrence analysis reveals four stable themes: (i) IoT infrastructure and ambient intelligence, (ii) energy management and smart grids, (iii) automation and intelligent building systems, and (iv) AI-based pattern recognition. We also document a shift from standalone automation to socio-technical, energy-aware ecosystems and outline practical implications around interoperability, privacy-by-design, and household-grid integration. All search strings, export settings, and cleaning rules are released for replication.\u003c/p\u003e","manuscriptTitle":"Smart homes in the Era of Sustainability and Digitalization: A Bibliometric Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-09 07:45:06","doi":"10.21203/rs.3.rs-8093804/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae1f97ab-bc9d-4332-a515-3f76cf1dee33","owner":[],"postedDate":"December 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:41:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-09 07:45:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8093804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8093804","identity":"rs-8093804","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}