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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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... The advent of versatile AI systems, which can handle multiple tasks with minimal retraining, has drastically lowered the entry barriers for implementing AI in different settings. Municipal governments have begun leveraging these AI capabilities across all domains including healthcare, education, environmental monitoring, waste management, transportation, risk assessment, and security, addressing both existing inefficiencies and emerging urban challenges (Herath & Mittal, 2022;Wu & Silva, 2010;Yigitcanlar et al., 2020Yigitcanlar et al., , 2024Zhou et al., 2019). ...
... Ethical concerns, including algorithmic bias, transparency, and data privacy, pose significant hurdles (Ahn & Chen, 2020;Campion et al., 2022;Reim et al., 2020;Sanchez et al., 2024;Tangi et al., 2023). In addition to these ethical challenges, municipalities also face practical barriers that complicate AI implementation, such as limited data availability, a shortage of AI expertise, high costs, and extended timelines and potential workforce displacement risks (Herath & Mittal, 2022;Rjab et al., 2023). In addition, the opacity of AI technology and its potential implications prevent widespread acceptance among developers, municipal employees and the public. ...
This research aims to develop a structured approach for implementing Artificial Intelligence (AI) in municipal governance. The study addresses three key questions: (1) What principles can be derived from existing AI implementation frameworks? (2) How should an approach for municipal AI projects be designed? (3) What are the main risks at each implementation stage? The research methodology combined three components: (1) a literature review of AI and software implementation approaches and municipal challenges, (2) analysis of findings from long-term collaborations with German municipalities and two specific AI implementation projects, and (3) low-threshold validation through two webinars with municipal representatives. The study produced an eight-phase implementation framework emphasizing iterative experimentation and risk awareness, while highlighting the distinct challenges of AI compared to traditional software implementation. Key phases include task identification, AI suitability assessment, data evaluation, solution development/procurement, MVP creation, testing, operational transition, and continuous monitoring. Each phase incorporates AI-specific steps and risk factors tailored to municipal contexts. While the framework provides practical guidance for municipal AI implementation, positioning cities for the gradual transition toward post-smart cities with AI-enabled governance, its current foundation primarily reflects German municipal experiences. Further research and case studies are needed to validate and adapt the framework for diverse global contexts.
... Our primary goal in doing this research is to provide a response to the following questions: How do smart grids aid in the creation of smart cities, and what effects will this integration have on the design of future urban environments? The results of this investigation could impact future urban planning and policy decisions and change the course of sustainable city development worldwide; hence, the stakes are pretty high [3] [4]. This paper is organized as follows: Section 2 provides background and context of the study. ...
This study presents a comprehensive exploration of smart grids within the framework of smart cities, highlighting their technological foundations, contributions to sustainability, and the challenges they face. The integration of cutting-edge technologies, which are essential to the effectiveness and efficiency of smart grids, is highlighted. The study emphasizes how intelligent networks significantly contribute to improving urban sustainability through energy optimization, carbon footprint reduction, and integration of renewable energies. The study discusses the difficulties in implementing smart grids, with a particular emphasis on cybersecurity, privacy, and the requirement for strong regulatory frameworks, even as it acknowledges the advantages of smart grids from an economic and environmental standpoint. It delves deeper into upcoming developments and advancements in innovative grid technology, highlighting government and policy's critical role in forming urban energy systems. The undergoing study's conclusion outlines the implications for energy policy and urban development. It suggests areas for more research, including consumer participation, policy effect studies, technological assessment, and long-term sustainability impacts.
... . Few among them are smart building 7 , smart home 8 , the smart city 9 , smart transport 10 , smart industrial monitoring 11 , smart industry automation 12 , smart healthcare 13 , smart grids 14 , and smart agriculture 15 . The other industry where smart technologies can be utilized is in monitoring the marine environment 16 . ...
Advancements in Artificial Intelligence, Machine Learning, and Deep Learning have paved the way for ample applications in real-time. One of the major applications of this advancement is the innovative systems influenced by the Internet of Everything (IoE). The IoE environment greatly relies on the interconnection of an enormous number of sensors that are used for collecting and transmitting data. The data gathered helps for monitoring, decision-making, and automation of smart systems in multi-disciplinary domains. These sensors operate on battery power. The battery life of the sensors limits their efficiency in operation. The mechanism for analyzing the Remaining Battery Life (RBL) plays a major role in optimizing the network performance, thereby ensuring the reliability and availability of data throughout. This work focuses on proposing a novel framework integrating pre-processing, standardization, encoding scheme, and predictive modeling that includes two algorithms, RFRImpute and MetaStackD, for predicting the RBL of sensors in any IoE device using a meta-learning-based deep ensemble approach blue for analyzing factors such as power consumption, environmental conditions, operational frequency, and workload patterns. Leveraging regression algorithms such as Random Forest, Gradient Boosting, Light Gradient Boosting, Categorical Boosting and Extreme Gradient Boosting, we have modeled the non-linear and temporal dynamics of sensor battery degradation, thereby enabling proactive maintenance strategies, dynamic energy management, and resource allocation. Experimental results on the real-world Chicago Park District Beach water IoE dataset validate the effectiveness of our proposed approach, showing a 1.4% improvement in accuracy over the traditional voting ensemble model and a 93.3% reduction in training time as well as prediction time. The model size is reduced by 95.23% when compared to traditional voting ensembles.
... AI is progressively emerging as a foundational technology in CIM, significantly enhancing urban perception, analysis, and decision making through advanced capabilities such as pattern recognition, predictive analytics, and automated decision support. The integration of AI not only elevates the efficiency of urban data processing but also facilitates more intelligent and granular city management [32,33]. Presently, AI applications in CIM predominantly utilize machine learning, deep learning, and data mining techniques to analyze the extensive urban datasets, offering forward-looking insights into urban development and generating optimized solutions. ...
With rapid urbanization exacerbating the challenges in resource allocation, environmental sustainability, and infrastructure management, City Information Modeling (CIM) has emerged as an indispensable digital solution for smart city development. CIM represents an advanced urban management paradigm that integrates Geographic Information Systems (GISs), Building Information Modeling (BIM), and the Internet of Things (IoT) to establish a multidimensional digital framework for comprehensive urban data management and intelligent decision making. While the existing research has primarily focused on technical architectures, governance models, and application scenarios, a systematic exploration of CIM’s data-driven characteristics remains limited. This paper reviews the evolution of CIM from a data-centric view introducing a research framework that systematically examines the data lifecycle, including acquisition, processing, analysis, and decision support. Furthermore, it explores the application of CIM in key areas such as smart transportation and digital twin cities, emphasizing its deep integration with big data, artificial intelligence (AI), and cloud computing to enhance urban governance and intelligent services. Despite its advancements, CIM faces critical challenges, including data security, privacy protection, and cross-sectoral data sharing. This survey highlights these limitations and points out the future research directions, including adaptive data infrastructure, ethical frameworks for urban data governance, intelligent decision-making systems leveraging multi-source heterogeneous data, and the integration of CIM with emerging technologies such as AI and blockchain. These innovations will enhance CIM’s capacity to support intelligent, resilient, and sustainable urban development. By establishing a theoretical foundation for CIM as a data-intensive framework, this survey provides valuable insights and forward-looking guidance for its continued research and practical implementation.
... It improves the efficiency of public transportation and aids in traffic flow regulation (Zafar, 2024). AI helps monitor air quality, manage waste and reduce pollution when combined with smart technologies like the Internet of Things (Herath & Mittal, 2022). Cities may expand using AI while conserving resources and preserving the environment. ...
Artificial intelligence is transforming the way cities operate by increasing efficiency and sustainability. Smart cities use artificial intelligence (AI) to optimize traffic flow, reduce energy usage, and improve public services. AI-powered systems process massive volumes of data in real time to improve urban planning and resource allocation. However, there are certain obstacles, such as data protection, ethical considerations, and the potential of employment displacement. This study investigates how AI contributes to smart cities and the limitations that must be overcome. Understanding these aspects enables urban planners to develop AI-powered solutions that promote sustainable and equitable city growth.
... These gaps highlight the need for frameworks that facilitate the seamless integration of AI and ML into health risk management systems. The Integration of AI in smart healthcare presented by Herath & Mittal, 2022, is shown in figure 2. Ethical and technical considerations also play a crucial role in the implementation of AI and ML in occupational health. Data privacy is a major concern, as wearable devices and sensors collect sensitive personal information that must be protected from misuse. ...
... Integration of AI in smart healthcare(Herath & Mittal, 2022). ...
Occupational diseases remain a significant challenge in high-risk industries, where hazardous working conditions expose employees to health risks that often go undetected until symptoms become severe. To address this, leveraging artificial intelligence (AI) and machine learning (ML) offers transformative potential for proactive health risk management by enabling predictive modeling, real-time monitoring, and data-driven decision-making. This study presents a conceptual framework for integrating AI and ML technologies to predict and mitigate occupational diseases in high-risk industries such as mining, construction, and manufacturing. The proposed framework encompasses three key components: data acquisition, predictive modeling, and intervention strategies. Data acquisition involves collecting real-time health and environmental data through wearable sensors, IoT-enabled devices, and workplace monitoring systems. Predictive modeling employs advanced ML algorithms, such as decision trees, neural networks, and support vector machines, to identify patterns and risk factors associated with occupational diseases. Intervention strategies leverage predictive insights to develop targeted prevention measures, such as redesigning work environments, optimizing workflows, and implementing personalized health interventions. A case study approach evaluates the framework's applicability, focusing on high-risk industries in Nigeria. Initial results demonstrate the feasibility of using AI-driven systems to identify early indicators of diseases such as respiratory disorders, musculoskeletal conditions, and noise-induced hearing loss. The findings also highlight the framework's potential to enhance workplace safety, reduce healthcare costs, and improve employee well-being by transitioning from reactive to proactive health management. The framework underscores the importance of cross-disciplinary collaboration among engineers, healthcare professionals, and policymakers to ensure effective implementation. Ethical considerations, such as data privacy and fairness, are also addressed to ensure equitable access and compliance with international health and safety standards. This conceptual framework lays the foundation for future research and policy development aimed at integrating AI and ML technologies into occupational health systems, particularly in resource-constrained settings, to foster safer and healthier work environments.
... In urban planning and infrastructure, there are also notable examples; by using data and knowledge acquired by AI, cities can shift to another level and have the potential to revolutionize city development, which will enable over 30% of smart city applications by 2025, including urban transportation solutions, significantly enhancing urban sustainability, social welfare, and vitality (Herath and Mittal, 2022). Furthermore, AI-enabled robots are deployed in the hospitality sector to provide personalized services and facilitate seamless guest experiences (Szpilko et al., 2023). ...
... Worldwide, non-renewable resources provide over eighty percent of the energy (2) . Additionally, cities are the residence of approximately 55% of the global population, a figure that is expected to increase to 68% by 2050 and continue to rise until it reaches 10.9 billion by 2100 (3) . There will be major ecological concerns brought on by the expected 2.5 billion people living in metropolitan areas by the year 2050 (4) . ...
Objectives: As conventional energy dwindles and environmental issues rise, solar energy gains traction for its sustainability. Rooftop potential, improved panels, affordability, and easy installation drive its urban adoption, necessitating accurate PV panel feasibility assessments. Methods: This study aimed at measuring rooftop solar PV potential and energy consumption and production using a GIS based tool within ArcGIS City Engine. Analysing the built footprint data enables the estimation of PV potential. Findings: Studying the Gandhinagar Smart City revealed the existence of 39,738 unique rooftops covering a total area of 56,976,756.77 square meters. Additionally, it was revealed that their annual PV potential was found to be 1,017.74 MWh, while the replacement potential dwarfs to 1,216.75 MWh every year. However, in terms of environmental benefits, it is estimated that a shift towards solar based energy systems in urban environments can mitigate AGCC effects by reducing annual CO2 emissions up to 10.40 million tons. Novelty: This study introduces a novel GIS-based approach within ArcGIS City Engine to accurately assess rooftop solar PV potential at an urban scale. The analysis of Gandhinagar Smart City provides a comprehensive evaluation of rooftop energy potential, distinguishing between generation capacity and replacement potential. Keywords: 3D City Modelling, CO2 Emission Reduction, Decentralized Energy Systems, GIS-Based Analysis, Rooftop Solar Potential
... 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.
