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Recently, the population density in cities has increased at a higher pace. According to the United Nations Population Fund, cities accommodated 3.3 billion people (54%) of the global population in 2014. By 2050, around 5 billion people (68%) will be residing in cities. In order to make lifestyles in cities more comfortable and cost-effective, the c...
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... For the degree of Architecture, IoT provides improvements in efficiency, sustainability, and user experience. With this technology, smart buildings can be managed, where connected sensors control systems, optimize energy efficiency [23], [24], enable structural monitoring, design based on data, manage waste by providing information to containers, and examine physical variables such as temperature, humidity, air quality, light, and sound [25], [26]. In architecture, the tools offered by IoT allow for the design of more user-centered efficient environments. ...
The Internet of Things (IoT) applications are pervasive across various sectors; however, there remains some resistance to its adoption. Education 4.0 promotes the full integration of new technologies, both as tools for learning and instruments for professional development. This work studies the influence of higher education on the willingness towards IoT adoption after hands-on learning experiences. The primary objective is to determine whether a correlation exists between IoT adoption and the education of university students from three distinct professional degrees. The methodology employed involves a practical class where students engage in developing applications for manual data collection. These applications are designed to send data to the Internet, which is then visualized through a web interface. Tailored to each respective degree, three similar applications are developed. For this research, M5 Stack Core2 kits are utilized, along with UIFLOW programming language and the ThingSpeak platform, operating under the MQTT protocol. Following the training, students complete a Technology Acceptance Model (TAM) survey for IoT. The analysis of the influence of higher education on IoT acceptance employs ANOVA to identify differences between group means. The results reveal statistically significant differences in IoT acceptance between students in Industrial and Architecture degrees.
... Examples of big data storage include MongoDB, Cassandra, and other types of NoSQL databases along with distributed file systems like Hadoop HDFS. These systems can store large amounts of data coming from sensors and fetch the data for efficient processing by an AI model [39][40][41]. ...
Background: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation—continuously monitoring key manufacturing parameters. Objective: This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, within the pharmaceutical sector. Results: Applications discussed herein focus on industrial predictive analytics and quality, underpinned by case studies showing improvements in product quality and reductions in downtime. Yet, many challenges remain, including data integration and the ethical implications of AI-driven decisions, and most of all, regulatory compliance. This review also discusses recent trends, such as AI in drug discovery and blockchain for data traceability, with the intent to outline the future of autonomous pharmaceutical manufacturing. Conclusions: In the end, this review points to basic frameworks and applications that illustrate ways to overcome existing barriers to production with increased efficiency, personalization, and sustainability.
... Frameworks have also been proposed for specific technical areas, such as sustainable hydropower plant operations [36], asthma attack prediction [37], machine learning on the edge [38], security, privacy and risks [39], and planning and management [40]. Focusing more closely on AI framework, several reviews present broad evidence supporting the effectiveness and practical value of AI frameworks [31,41,42]. In terms of applications, the most impact from AI is reported in healthcare, mobility, privacy, and energy, while the notion of an 'urban artificial intelligence' has also been proposed through developments in autonomous cars, robots, and the built environment. ...
Smart cities are Hyper-Connected Digital Environments (HCDEs) that transcend the boundaries of natural, human-made, social, virtual, and artificial environments. Human activities are no longer confined to a single environment as our presence and interactions are represented and interconnected across HCDEs. The data streams and repositories of HCDEs provide opportunities for the responsible application of Artificial Intelligence (AI) that generates unique insights into the constituent environments and the interplay across constituents. The translation of data into insights poses several complex challenges originating in data generation and then propagating through the computational layers to decision outcomes. To address these challenges, this article presents the design and development of a Hyper-Automated AI framework with Generative AI agents for sustainable smart cities. The framework is empirically evaluated in the living lab setting of a ‘University City of the Future’. The developed AI framework is grounded on the core capabilities of acquisition, preparation, orchestration, dissemination, and retrospection, with an independent cognitive engine for hyper-automation of these AI capabilities using Generative AI. Hyper-automation output feeds into a human-in-the-loop process prior to decision-making outcomes. More broadly, this framework aims to provide a validated pathway for university cities of the future to take up the role of prototypes that deliver evidence-based guidelines for the development and management of sustainable smart cities.
... Facial recognition 16 Classification performance of ML models 17 ...
Lighting Systems (LSs) play a fundamental role in almost every aspect of human activities. Since the advent of lights, both academia and industry have been engaged in raising the quality of the service offered by these systems. The advent of Light Emitting Diode (LED) lighting represented a giant step forward for such systems in terms of light quality and energy saving. To further raise the quality of the services offered by LSs, increase the range of services they offer, while at the same time consolidating their reliability and security, we see the need to explore the contribution that can be derived from the use of the Artificial Intelligence of Things (AIoT) emerging technology. This paper systematically reviews and compares the state-of-the-art with regard to the impact of the AIoT in the smart LS domain. The study reveals that the field is relatively new, in fact the first works date back to 2019. In addition to that, the review delves into recent research works focusing on the usage of Machine Learning (ML) algorithms in an edge Cloud-based computing architecture. Our findings reveal that this topic is almost unexplored. Finally, the survey sheds light on future research opportunities that can overcome the current gaps, with the final aim of guiding scholars and practitioners in advancing the field of smart LSs. The study is reported in full detail, so it can be replicated.
... های روش :2 سطح تحلیلی 18 )%29( Nikitas et al. 2020, Syed et al. 2021, Serban & Lytras 2020, Golubchikov & Thornbush 2020, Herath. & Mittal 2022, Omitaomu & Niu 2021, Verma 2022, Kuguoglu et al. 2021, Osamy et al. 2022, Zamponi & Barbierato 2022. Alahi et al. 2023, Bokhari & Myeong 2023, Lauri et al. 2023 خاص های نظریه :3 سطح 28 )%6 ( Allam & Dhunny 2019, Ullah et al. 2020, Cugurullo 2020, Chui et al. 2018, Yigitcanlar & Cugurullo 2020, Zhang et al. 2021, Guo et al. 2018 ...
Artificial intelligence offers highly suitable solutions for numerous challenges in urban transformation and development, such as ensuring an adequate water supply, energy management, waste management, and reducing traffic congestion, noise, and pollution. Given the social and technical nature of smart cities and the applications of artificial intelligence in this field, university research has seen a significant increase in recent years. Furthermore, an analysis of the popularity of keywords "smart city" and "artificial intelligence" on Google Trends indicates that these key terms have been increasingly popular from 2014 to the present. Therefore, this article systematically examines the current status and future directions of research on the application of artificial intelligence in smart cities through a sequential review method. To this end, the databases "Scopus" and "Google Scholar" were searched, identifying a total of 3384 articles. Following a systematic review and final screening process, 61 articles were selected for analysis. The findings show an increasing trend in research publications in this area from 2018 onwards. Additionally, an examination of the thematic scope of selected research indicates that the application of artificial intelligence in smart cities is predominantly focused on urban management and sustainable development (30%), smart living and intelligent infrastructures (28%), and intelligent environment (21%). The results also reveal that 64% of studies have employed qualitative methods, 21% quantitative methods, and 15% a combination of methods. As the application of artificial intelligence in smart cities is still in the conceptualization stage, the noticeable preference for qualitative methods among researchers in this
... AI borderline is transforming the smart city foundation by permissive honest-occasion accountability, lowering abeyance, optimizing frequency range exercise, and reconstructing solitude and security. This example change authorizes smart places to run more capably, behave swiftly in changeful environments, and enhance the overall characteristics of city life (Herath & Mittal, 2022). ...
The healthcare and industry fields are being significantly impacted by the dynamic synergy of edge computing and artificial intelligence (AI) algorithms. The complex AI algorithms at the center of this revolution are designed for edge computing. This chapter's goal is to provide an understanding of these algorithms' capabilities in practical applications by analyzing them in terms of accuracy, speed, and resource consumption. By using edge AI, AI calculations can now take place locally on devices instead of being limited to centralized cloud servers. This makes use of the complementary properties of edge computing and data processing power. It entails the creation and implementation of cutting-edge computer programs and algorithms that work directly on adjacent computing endpoints, Internet of Things (IoT) devices, and smartphones. This is different from the traditional use of cloud servers that are centralized. Due to the complicatedness of most AI models and the challenge of calculating conclusion results on devices accompanying restricted possessions, AI duties are currently achieved to process requests in cloud dossier centers. However, these "end cloud" architectures cannot meet the needs of palpable-period AI services to a degree of instant science of logical analysis and astute production. Therefore, bringing AI requests to the edge opens the potential for AI request synopsizes, especially in conditions of attaining reduced abeyance traits (Wang et al., 2020). Edge AI enhances the effectiveness of data processing. The system may manage data more effectively, maximizing resource utilization and guaranteeing that crucial insights are obtained quickly, by dividing computing responsibilities across local devices. This paradigm shift toward edge AI is in line with the changing demands of industry and presents a viable path forward for breakthroughs across a range of fields, including healthcare where secure and quick data processing is critical. Figure 8.1 displays the direct deployment of artificial intelligence algorithms into edge devices. Edge AI is independent of the cloud so it can process data more quickly and efficiently from its source. Edge AI enhances user experience and uses local processing to boost data protection. Since there is no requirement for data transfer, security precautions are increased in contrast to centralized cloud systems. From a technical perspective, less bandwidth needed can result in cheaper rates for Internet services that require subscriptions. One unique characteristic of edge technology gadgets is their self-contained design. These gadgets function independently and do not require continuous assistance from data scientists or AI developers, in contrast to typical setups. Monitoring becomes more efficient when graphical data flows are provisioned automatically (Raj et al., 2022).
... Through the use of AI, intelligent systems [7][8][9], virtual agents and assistants [10][11][12], and multiagent systems [13][14][15] can be created. Recent literature review studies have explored its use in various domains, such as education [16][17][18], industry [19][20][21], healthcare [22][23][24], business [25][26][27], smart cities [28][29][30], etc. The outcomes of these studies highlight the potential of AI to transform and enrich various sectors, which, in turn, reveals the need to further explore its capabilities to be used in combination with other novel technologies to further amplify its impact. ...
The research into artificial intelligence (AI), the metaverse, and extended reality (XR) technologies, such as augmented reality (AR), virtual reality (VR), and mixed reality (MR), has been expanding over the recent years. This study aims to provide an overview regarding the combination of AI with XR technologies and the metaverse through the examination of 880 articles using different approaches. The field has experienced a 91.29% increase in its annual growth rate, and although it is still in its infancy, the outcomes of this study highlight the potential of these technologies to be effectively combined and applied in various domains transforming and enriching them. Through content analysis and topic modeling, the main topics and areas in which this combination is mostly being researched and applied are as follows: (1) “Education/Learning/Training”, (2) “Healthcare and Medicine”, (3) “Generative artificial intelligence/Large language models”, (4) “Virtual worlds/Virtual avatars/Virtual assistants”, (5) “Human-computer interaction”, (6) “Machine learning/Deep learning/Neural networks”, (7) “Communication networks”, (8) “Industry”, (9) “Manufacturing”, (10) “E-commerce”, (11) “Entertainment”, (12) “Smart cities”, and (13) “New technologies” (e.g., digital twins, blockchain, internet of things, etc.). The study explores the documents through various dimensions and concludes by presenting the existing limitations, identifying key challenges, and providing suggestions for future research.
... Comprehensive research analyzing AI-driven traffic management implementations across 127 metropolitan areas reveals transformative improvements in urban mobility [5]. Cities implementing sophisticated machine learning algorithms for real-time traffic flow optimization report an average reduction of 35.3% in peak-hour congestion and a 29.1% decrease in travel times. ...
... Predictive congestion management systems demonstrate exceptional effectiveness in urban environments, with implementations reducing traffic incidents by 43.5% and improving emergency response times by 38.2%. Advanced pattern analysis capabilities enable these systems to process approximately 2.5 million vehicle movements daily, maintaining accuracy rates of 96.2% [5] in identifying potential congestion points. The study particularly emphasizes the success of adaptive traffic signal control systems, which show a 46.8% reduction in average wait times at intersections during peak hours and a 28.4% decrease in vehicle emissions through optimized flow management. ...
This article presents a comprehensive analysis of Artificial Intelligence (AI) integration with cellular wireless technologies in smart city environments, examining their impact on urban infrastructure optimization. Through analysis of implementations across numerous smart city initiatives in Europe and North America, the article demonstrates significant improvements in operational efficiency and resource utilization. The article reveals that edge computing implementations achieve substantial processing time reductions, while 5G networks support massive device densities per square kilometer with exceptional reliability. Traffic management systems show significant reduction in peak-hour congestion, while public safety systems achieve impressive threat detection rates. Energy management implementations demonstrate high optimization rates in distribution, and water management systems achieve excellent leak detection accuracy. The article particularly emphasizes the role of integrated AI and cellular technologies in achieving sustainable urban development, with implementations showing considerable reduction in energy consumption and decrease in carbon emissions. These findings provide valuable insights for urban planners and technology implementers, establishing benchmarks for future smart city developments.
... Unsecured communication protocols like Message Queuing Telemetry Transport (MQTT) can compromise data integrity and jeopardize public safety. Attackers can exploit these weaknesses to disrupt essential services, steal data, or gain unauthorized access [103]. These vulnerabilities can severely affect city operations [9][31] [50]. ...
... These technologies enable organizations to analyze patterns and detect anomalies instantly, enhancing their ability to respond quickly and effectively to potential threats while minimizing the impact of cyber incidents [59]. Integrating AI and machine learning into cybersecurity frameworks significantly advances safeguarding urban infrastructures against emerging threats [7] [103]. Various machine learning methods, including K-nearest neighbor, Extreme Gradient Boosting, decision trees, and random forests, are crucial in threat detection. ...
Smart cities rapidly evolve into transformative ecosystems where advanced technologies work together to improve urban living. These interconnected environments use emerging technologies to offer efficient services and sustainable solutions for urban challenges. As these systems become more complex, their vulnerability to cybersecurity threats also increases. Integrating artificial intelligence (AI) and Blockchain technologies to address these challenges presents promising solutions that ensure secure and resilient infrastructures. This study provides a comprehensive survey of integrating AI, Blockchain, cybersecurity, and smart city technologies based on an analysis of peer-reviewed journals, conference proceedings, book chapters, and websites. Seven independent researchers reviewed relevant literature published between January 2021 and December 2024 using ACM Digital Library, Wiley Online Library, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, IGI Global, and Google Scholar. The study explores how AI can enhance threat detection, anomaly detection, and predictive analytics, enabling real-time responses to cyber threats. It examines various AI methodologies, including machine learning and deep learning, to identify vulnerabilities and prevent attacks. It discusses the role of Blockchain in securing data integrity, improving transparency, and providing decentralized control over sensitive information. Blockchain’s tamper-proof ledger and smart contract capabilities offer innovative solutions for identity management, secure transactions, and data sharing among smart city stakeholders. The study also highlights how combining AI and Blockchain can create robust cybersecurity frameworks, enhancing resilience against emerging threats. The survey concludes by outlining future research directions and offering recommendations for policymakers, urban planners, and cybersecurity professionals. This study identifies emerging trends and applications for enhancing the security and resilience of smart cities through innovative technological solutions. The survey provides valuable insights for researchers and practitioners who aim to utilize AI and Blockchain to improve smart city cybersecurity.
... Technologies such as artificial intelligence (AI) and the Internet of Things have the potential to transform cities into more sustainable smart cities. Herath et al. (2022) discuss the application of AI in smart cities, including in key areas like healthcare, education, environmental and waste management, mobility and smart transportation, agriculture, risk management, and security (Herath & Mittal, 2022). The integration of AI into smart cities can be beneficial by automating operations, reducing human error, making decisions based on valid data, and improving the environment through different systems. ...
... Technologies such as artificial intelligence (AI) and the Internet of Things have the potential to transform cities into more sustainable smart cities. Herath et al. (2022) discuss the application of AI in smart cities, including in key areas like healthcare, education, environmental and waste management, mobility and smart transportation, agriculture, risk management, and security (Herath & Mittal, 2022). The integration of AI into smart cities can be beneficial by automating operations, reducing human error, making decisions based on valid data, and improving the environment through different systems. ...
The purpose of this study was to consolidate machine learning applications and develop a method to simultaneously analyze unstructured text and images pertaining to travel and tourism. This paper extracted city-related tourist-generated content from social media posts and analyzed this content to elucidate public perception of Taipei and identify the factors that make these posts attractive. Amidst the global COVID-19 pandemic of the early 2020s, this study examines social media discourse on urban topics. Focused on the period from 2019 to 2020, it compares content to discern shifts in societal concerns amidst the pandemic’s progression. The analysis aims to illuminate evolving thematic patterns within city-related discussions against the backdrop of this unprecedented public health crisis. Several techniques and technologies, including content mining, Google Cloud Vision AI, topic modeling, and artificial intelligence machine learning were adopted to analyze the images and interactive characteristics of tourist-generated content relating to the city imagery and tourism transformation of Taipei. The data analyzed in this study was collected from Facebook, and RapidMiner was employed as the mining environment to apply topic modeling to identify the topics in tourist-generated content relating to Taipei before and during the pandemic and elucidate expectations and topic evolutions; and extract meaning images and text from the topics and combine them with interactive data from social media posts to identify the topics inductive to the public at different periods of the pandemic. The main graphic theme before the epidemic was to convey the charm of Taipei, compared to the graphic theme during the epidemic, which shifted to a nature-based image.
