AI in smart cities with cellular wireless technologies: optimizing urban infrastructure

Cloud computing has revolutionized healthcare service delivery by enabling advanced technological capabilities, enhanced data management, and improved patient care outcomes. The transformation encompasses various domains, including infrastructure modernization, security enhancement, and application development. Healthcare organizations have witnessed significant improvements in operational efficiency, resource utilization, and service delivery through cloud adoption. The integration of artificial intelligence, machine learning, and telemedicine capabilities has further enhanced diagnostic accuracy and patient care quality. While technical challenges exist in areas of data migration, security compliance, and system optimization, the benefits of cloud computing continue to drive innovation in healthcare services. The implementation of cloud-based solutions has particularly excelled in supporting remote healthcare delivery, enabling real-time patient monitoring, and facilitating seamless information exchange across healthcare networks. The adoption of standardized protocols and advanced security frameworks has established a robust foundation for future healthcare technology advancement, ensuring sustainable growth and continuous improvement in healthcare service delivery.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into Enterprise Resource Planning (ERP) systems represents a paradigmatic shift in organizational digital transformation strategies. This research investigates the revolutionary impact of AI/ML integration within SAP ecosystems, examining performance improvements, operational efficiency gains, and strategic advantages. Through comprehensive analysis of implementation patterns across diverse industry sectors, this study presents empirical evidence demonstrating significant enhancements in predictive analytics, automated decision-making, and resource optimization. The findings reveal that organizations implementing AI-enabled SAP solutions experience an average 35% improvement in operational efficiency, 42% reduction in manual processing time, and 28% increase in predictive accuracy for demand forecasting. This research contributes to the growing body of knowledge on intelligent ERP systems and provides practical insights for enterprise digital transformation initiatives.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT), known as AIoT, is driving significant advancements in smart collaboration across industries. By combining IoT's data-gathering capabilities with AI's analytical power, AIoT systems enable real-time decision-making, automation, and optimization in areas like manufacturing, healthcare, and smart cities. This chapter examines how AI interprets IoT-generated data to enhance efficiency, sustainability, and productivity. Key use cases are highlighted, demonstrating the synergy between AI and IoT in improving processes, reducing energy consumption, and bolstering safety measures. Challenges such as data security, latency, and device compatibility are addressed, with potential solutions like edge computing and secure frameworks proposed. The chapter concludes with a forward-looking perspective on how AIoT can transform industries and reshape human-machine collaboration, paving the way for smarter, more connected environments.

As the global demand for clean, sustainable energy continues to rise, maintaining the reliability and efficiency of renewable energy systems has never been more critical. This study explores how the combined power of robotics and artificial intelligence (AI) can significantly improve the maintenance of wind and solar energy infrastructure. By uniting the physical precision of robotics with the analytical strength of AI, we propose a next-generation approach to renewable energy maintenance that is smarter, faster, and more cost-effective. At the heart of this approach is the integration of robotic systems for real-time inspection, cleaning, and monitoring of wind turbines and solar panels, working in tandem with AI-driven predictive maintenance tools. These tools, powered by machine learning algorithms, analyze sensor data to detect early signs of equipment failure well before breakdowns occur. This proactive strategy not only minimizes unplanned downtime but also allows for more strategic allocation of maintenance resources, reducing both labour and material costs. Our research involved reviewing current technological applications, modelling AI maintenance workflows, and analyzing the impact of autonomous systems in real-world scenarios. The findings show substantial improvements in operational efficiency, with AI-enhanced systems achieving up to a 25% increase in performance reliability, significant labour cost reductions, and better fault detection accuracy compared to traditional methods. Robotic solutions also contributed to water conservation in solar panel maintenance and reduced human exposure to hazardous maintenance environments. The integration of robotics and artificial intelligence (AI) has the potential to revolutionize the maintenance of renewable energy systems, particularly in wind and solar power. This paper proposes a synergistic approach to optimizing renewable energy maintenance by leveraging the strengths of both robotics and AI. By combining advanced robotics technologies with AI-powered predictive analytics, this approach aims to improve the efficiency, reliability, and sustainability of wind and solar power systems. The proposed system utilizes robotic inspection and monitoring, AI-driven predictive maintenance, and machine learning algorithms to detect anomalies and optimize performance. The results of this study demonstrate the potential for significant improvements in energy output, reduced maintenance costs, and enhanced system reliability. This research contributes to the development of next-generation renewable energy systems that are more efficient, sustainable, and resilient.

Introduction Today, the rapid acceleration of urbanization has made it necessary to reconsider the balance among energy consumption, environmental sustainability, and quality of life. Buildings, which account for a significant share of cities’ carbon footprint, play a critical role in efforts to improve energy efficiency and ensure user well-being. In this context, advances in digitalization and Internet of Things (IoT) technologies have enabled buildings to evolve beyond mere physical structures into dynamic, data-driven, and user-interactive systems. Within this framework, the present study constitutes a systematic literature review addressing the effects of indoor environment design in smart cities on energy efficiency (E), user comfort (C), and environmental sustainability (S). In recent years, IoT-based sensor and control technologies have reconfigured approaches to energy management in buildings by enabling the continuous monitoring of environmental parameters such as temperature, humidity, carbon dioxide levels, lighting, and movement, while also strengthening user experience through a holistic perspective. Methods In this regard, the study examined 76 different works in the literature, including field applications, experimental research, and conceptual models. These studies were evaluated through an inductive thematic analysis approach based on content and classified according to recurring conceptual clusters in the literature. Results An examination of these sources reveals that the contributions of IoT technologies to smart buildings and cities are multidimensional in nature. This extensive body of knowledge in the literature demonstrates that IoT is not merely a technical infrastructure but also an ecosystem that transforms energy, health, the environment, transportation, and social life. It is evident that the data derived from all reviewed studies were synthesized under the headings of energy efficiency, indoor environmental quality and comfort, smart city infrastructure, user interaction, security/facility management, and industrial applications. Accordingly, the present study evaluates the contributions of IoT-based solutions to reducing energy consumption, improving environmental conditions, and supporting user-centered indoor design. The reviewed studies show that these technologies not only enhance energy efficiency (maximum savings: smart parking [92.6%] and smart lighting [73.2%]; average building savings: 20–30% through BEMS and IoT systems; HVAC optimization: 30–70% through artificial intelligence), but also support user health and comfort (the use of smart systems is generally expected to produce an improvement of more than 20% in comfort levels, while this rate can reach the 70–90% range with advanced personalized models). Furthermore, they demonstrate that IoT-based systems play a strategic role in achieving environmental sustainability goals, reducing carbon emissions, and implementing smart city policies. Discussion The original contribution of this study lies in its systematic synthesis of energy, comfort, and sustainability within an integrated thematic classification framework, thereby revealing trends in the field, research gaps, and potential future directions.

Smart cities have expanded rapidly with the introduction of Internet of Things (IoT) devices and edge computing techniques. Such technologies have transformed how cities function through the ability to deliver smart services and analyze data in real time. But that advancement also adds to the demanding issues about privacy and data security. In smart city environments, privacy-preserving is essential to protect sensitive data collected from different channels against leakage, abuse and unauthorized access. This work is a review of data security and privacy-preserving models made for edge computing and smart cities. It examines the advantages and disadvantages of existing machine learning, blockchain, cryptography, hybrid architectures of optimizing resources, scalability and efficiency. The review discusses open challenges, including interoperability, resource constraints and governance issues, as well as future research directions such as quantum-secure security, federated learning and AI-based adaptive models. The concepts aim to help researchers and policymakers establish strong, durable smart city infrastructures.

Background: The integration of artificial intelligence (AI) in the organization of nursing care has continually evolved, driven by the need for innovative solutions to ensure quality of care. The aim is to synthesize the evidence on the use of artificial intelligence in the organization of nursing care. Methods: A scoping review was carried out based on the Joanna Briggs Institute methodology, following the PRISMA-ScR guidelines, in the MEDLINE, CINAHL Complete, Business Source Ultimate and Scopus® databases. We used ProQuest—Dissertations and Theses to search gray literature. Results: Ten studies were evaluated, identifying AI-mediated tools used in the organization of nursing care, and synthesized into three tool models, namely monitoring and prediction, decision support, and interaction and communication technologies. The contributions of using these tools in the organization of nursing care include improvements in operational efficiency, decision support and diagnostic accuracy, advanced interaction and efficient communication, logistical support, workload relief, and ongoing professional development. Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.

Background: The integration of Artificial Intelligence (AI) and automation in healthcare has emerged as a transformative force, reshaping the modern medical environment. These technologies promise to enhance operational efficiency, improve diagnostic accuracy, and optimize patient care. However, their adoption also raises ethical, technical, and operational challenges. Objective: This systematic review aims to explore the impact of AI and automation on the healthcare sector, focusing on their applications, benefits, challenges, and future potential. Methods: A systematic search was conducted across major databases, including PubMed, Scopus, and Web of Science, to identify peer-reviewed studies published between 2016 and 2024. Studies were selected based on their relevance to AI and automation in medical environments, using predefined inclusion and exclusion criteria. Data were extracted and analyzed to identify key themes. Results: The review identified significant advancements in AI-driven diagnostics, robotic-assisted surgeries, workflow automation, and predictive analytics. These technologies have demonstrated measurable improvements in clinical outcomes, operational efficiency, and cost reduction. However, challenges such as data privacy, ethical concerns, and integration issues persist. Conclusion: AI and automation are revolutionizing healthcare by streamlining processes, enhancing patient outcomes, and addressing resource limitations. Despite current challenges, their potential to transform the medical environment is undeniable. Further research and multidisciplinary collaboration are essential to overcome barriers and fully harness these technologies.

Risk management and industrial maintenance have evolved into proactive approaches supported by emerging technologies. This article explores the integration of mathematical models and artificial intelligence (AI) techniques in the prediction and prevention of industrial accidents. Through systematic review, predictive modeling, and case study analysis, significant improvements in operational efficiency and occupational safety are evidenced. It is concluded that AI and quantitative models are key tools in the digital transformation of industrial maintenance.

Wireless Cellular Technology (WCT) has advanced rapidly in both the USA and the European Union; however, the two are pursuing different public policy and business market approaches in developing, adopting and deploying the technology. Efforts to promote the commercialisation of the technology to further wireless enhanced emergency call services policy also differ. We explore existing mandates and the current state of cellular technology. Factors influencing WCT's development are modelled, illustrating how government intervention has affected the technology.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

As governments and private companies alike race to achieve the vision of a smart city — where artificial intelligence (AI) technology is used to enable self-driving cars, cashier-less shopping experiences and connected home devices from thermostats to robot vacuum cleaners — advancements are being made in both software and hardware to enable increasingly real-time, accurate inference at the edge. One hardware solution adopted for this purpose is the LiDAR sensor, which utilizes infrared lasers to accurately detect and map its surroundings in 3D. On the software side, developers have turned to artificial neural networks to make predictions and recommendations with high accuracy. These neural networks have the potential, particularly run on purpose-built hardware such as GPUs and TPUs, to make inferences in near real-time, allowing the AI models to serve as a usable interface for real-world interactions with other AI-powered devices, or with human users. This paper aims to example the joint use of LiDAR sensors and AI to understand its importance in smart city environments.

Many cities around the world have embarked on ambitious programmes towards creating Smart Cities where information, diverse digital opportunities, and collective intelligence can be harnessed ubiquitously. Smart Cities are conceptualized using citywide smart and intelligent architectures informed by the context in which they are implemented. These architectures make it possible to access information and intelligence anywhere and at any time. Information processing and computing is embedded within the urban infrastructures to a point where immovable city entities such as traffic lights are more intelligent to make real-time decisions based on the happenings in the environment in which they are deployed. Advanced development of ambient computing within the realm of Smart Cities will further culminate into possibilities such as vehicle-to-vehicle communication (V2V) and mobile-to-mobile (M2M) communication. Using extensive and critical literature review, this chapter specifically focusses on the design of information architectures that will ultimately support the enshrining of spatial intelligence within Smart City environments hinged on the internet of things (IoT) and cloud/fog computing. The chapter presents latest trends in the research and practice of ambient intelligence (AmI) linked to the realization of the key principles of Smart Cities from the information topology and architecture point of view. A conceptual ambient intelligence architecture that highlights the building blocks of any ambient intelligence architecture as deployed in Smart City environments is also proposed. The proposed conceptual architecture can be used as a blueprint in the design of ambient intelligence topologies and architectures in different contextual settings.

Smart cities enhance the quality of life for citizens by utilising cutting edge technologies such as 5G and beyond wireless communication. Internet of Everything (IoE) enables a smart city to power and monitor multiple geographically distributed IoE nodes to support a range of applications across various domains such as energy and resource management, intelligent transport systems and E-health to name a few. Due to unprecedented increase in the use of IoE technology and the volume of data it generates, there is need to develop a state-of-the-art architecture to support wide range of applications in order to manage smart city resources in an efficient and intelligent manner. In this work in progress article, we present a conceptual design to establish efficient task off-loading and resource allocation architecture for smart city environment. We first present a novel conceptual design, called conventional model for task off-loading and resource allocation. Secondly, we build upon the conventional model to introduce the intelligence for task off-loading and resource allocation problem. We further develop the specific research questions in order to design and evaluate the performance of various units within the above mentioned models to accommodate the technological advancements such as the use of Artificial Intelligence (AI) in the sixth generation (6G) wireless communication era.

The government announced the fourth comprehensive plan for smart cities in May, following the enactment of the world's first smart city-related law in 2008 and the implementation of smart city policies.Although it has led to the development of smart infrastructure that integrates information and communication technology into new cities, the government's top-down approach has also faced criticism.Recently, the significance of the Smart City Living Lab, involving citizen participation, has gained prominence in the evolving landscape of smart city policies.Initiatives like the smart city challenge and integrated platform supply projects underscore the shift toward citizen engagement.The government is now advocating for a smart city convergence course tailored to university characteristics to nurture innovative talents for smart cities.Domestic universities have started to embrace living labs in response to this trend.This study proposes a policy direction for the efficient operation of the Smart City Living Lab project, which local governments increasingly adopt.It also delves into the management of the Resident-Participatory Living Lab, which is university-led and involves citizen participation and its implications.This study of living labs in Dangjin, this research explores strategies to activate and manage living labs at domestic universities through a case study of living labs in Dangjin city.