IoT-Enabled Smart Cities: Evolution and Outlook

The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative models. These advancements are reshaping data-driven planning strategies, practices, and approaches, thereby facilitating the achievement of environmental sustainability goals. This transformative wave signals a fundamental shift - marked by the synergistic operation of artificial intelligence (AI), artificial intelligence of things (AIoT), and urban digital twin (UDT) technologies. While previous research has largely explored urban AI, urban AIoT, and UDT in isolation, a significant knowledge gap exists regarding their synergistic interplay, collaborative integration, and collective impact on data-driven environmental planning in the dynamic context of sustainable smart cities. To address this gap, this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies, models, and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart cities. Central to this study are four guiding research questions: 1. What theoretical and practical foundations underpin the convergence of AI, AIoT, UDT, data-driven planning, and environmental sustainability in sustainable smart cities, and how can these components be synthesized into a novel comprehensive framework? 2. How does integrating AI and AIoT reshape the landscape of data-driven planning to improve the environmental performance of sustainable smart cities? 3. How can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities? 4. What challenges and barriers arise in integrating and implementing AI, AIoT, and UDT in data-driven environmental urban planning, and what strategies can be devised to surmount or mitigate them? Methodologically, this study involves a rigorous analysis and synthesis of studies published between January 2019 and December 2023, comprising an extensive body of literature totaling 185 studies. The findings of this study surpass mere interdisciplinary theoretical enrichment, offering valuable insights into the transformative potential of integrating AI, AIoT, and UDT technologies to advance sustainable urban development practices. By enhancing data-driven environmental planning processes, these integrated technologies and models offer innovative solutions to address complex environmental challenges. However, this endeavor is fraught with formidable challenges and complexities that require careful navigation and mitigation to achieve desired outcomes. This study serves as a comprehensive reference guide, spurring groundbreaking research endeavors, stimulating practical implementations, informing strategic initiatives, and shaping policy formulations in sustainable urban development. These insights have profound implications for researchers, practitioners, and policymakers, providing a roadmap for fostering resiliently designed, technologically advanced, and environmentally conscious urban environments.

A novel single-channel edge computing LoRa gateway for real-time confirmed messaging

LoRaWAN has become the technology of choice for increasing Internet of Things applications owing to its long range and low power consumption characteristics. However, in the uplink confirmed messaging cases, the entire retransmission could take several seconds, so it cannot be used in scenarios that require rapid confirmed messaging, such as emergency alerting and real-time controlling applications. Nevertheless, there has been limited work targeting this issue. This study presents a novel LoRaWAN gateway using edge computing to expedite the confirmed messaging process by generating the acknowledgment (ACK) locally, so that the confirmed messaging time can be significantly reduced. Additionally, the resource utilization of the network server can also be decreased due to the use of edge computing. We verified the effectiveness of our solution through extensive simulations and experiments. The confirmed messaging time between the end nodes and the gateway averaged 43 ms for a maximum of 2 retransmissions. With the adoption of edge computing on the gateway, the network server's central processing unit (CPU), memory, and bandwidth peak utilization decrease from 53.51 to 39.46, 73.88 to 72.11%, and 4422.68 kbps to 3271.27 kbps, respectively. In addition, the network server's system load decreases from 2.15 to 1.69, while the gateway cost is reduced by almost $ 38 compared to the benchmark products.

Towards an AI-Driven Data Reduction Framework for Smart City Applications

The accelerated development of technologies within the Internet of Things landscape has led to an exponential boost in the volume of heterogeneous data generated by interconnected sensors, particularly in scenarios with multiple data sources as in smart cities. Transferring, processing, and storing a vast amount of sensed data poses significant challenges for Internet of Things systems. In this sense, data reduction techniques based on artificial intelligence have emerged as promising solutions to address these challenges, alleviating the burden on the required storage, bandwidth, and computational resources. This article proposes a framework that exploits the concept of data reduction to decrease the amount of heterogeneous data in certain applications. A machine learning model that predicts a distortion rate and its corresponding reduction rate of the imputed data is also proposed, which uses the predicted values to select, among many reduction techniques, the most suitable approach. To support such a decision, the model also considers the context of the data producer that dictates the class of reduction algorithm that is allowed to be applied to the input stream. The achieved results indicate that the Huffman algorithm performed better considering the reduction of time-series data, with significant potential applications for smart city scenarios.

Smart city healthcare delivery innovations: a systematic review of essential technologies and indicators for developing nations

BACKGROUND

In recent times, the concept of smart cities has gained remarkable traction globally, driven by the increasing interest in employing technology to address various urban challenges, particularly in the healthcare domain. Smart cities are proving to be transformative, utilizing an extensive array of technological tools and processes to improve healthcare accessibility, optimize patient outcomes, reduce costs, and enhance overall efficiency.

METHODS

This article delves into the profound impact of smart cities on the healthcare landscape and discusses its potential implications for the future of healthcare delivery. Moreover, the study explores the necessary infrastructure required for developing countries to establish smart cities capable of providing intelligent health and care services. To ensure a comprehensive analysis, we employed a well-structured search strategy across esteemed databases, including PubMed, OVID, EMBASE, Web of Science, and Scopus. The search scope encompassed articles published up to November 2022, resulting in a meticulous review of 22 relevant articles.

RESULTS

Our findings provide compelling evidence of the pivotal role that smart city technology plays in elevating healthcare delivery, forging a path towards improved accessibility, efficiency, and quality of care for communities worldwide. By harnessing the power of data analytics, Internet of Things (IoT) sensors, and mobile applications, smart cities are driving real-time health monitoring, early disease detection, and personalized treatment approaches.

CONCLUSION

Smart cities possess the transformative potential to reshape healthcare practices, providing developing nations with invaluable opportunities to establish intelligent and adaptable healthcare systems customized to their distinct requirements and limitations. Moreover, the implementation of smart healthcare systems in developing nations can lead to enhanced healthcare accessibility and affordability, as the integration of technology can optimize resource allocation and improve the overall efficiency of healthcare services. It also may help alleviate the burden on overburdened healthcare facilities by streamlining patient care processes and reducing wait times, ensuring that medical attention reaches those in need more swiftly.

Cybersecurity and Cyber Forensics for Smart Cities: A Comprehensive Literature Review and Survey

Smart technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are being adopted in cities and transforming them into smart cities. In smart cities, various network technologies, such as the Internet and IoT, are combined to exchange real-time information, making the everyday lives of their residents more convenient. However, there is a lack of systematic research on cybersecurity and cyber forensics in smart cities. This paper presents a comprehensive review and survey of cybersecurity and cyber forensics for smart cities. We analysed 154 papers that were published from 2015 to 2022 and proposed a new framework based on a decade of related research papers. We identified four major areas and eleven sub-areas for smart cities. We found that smart homes and the IoT were the most active research areas within the cybersecurity field. Additionally, we found that research on cyber forensics for smart cities was relatively limited compared to that on cybersecurity. Since 2020, there have been many studies on the IoT (which is a technological component of smart cities) that have utilized machine learning and deep learning. Due to the transmission of large-scale data through IoT devices in smart cities, ML and DL are expected to continue playing critical roles in smart city research.

Wastewater-based epidemiology for preventing outbreaks and epidemics in Latin America - Lessons from the past and a look to the future

Wastewater-based epidemiology (WBE) is an approach with the potential to complement clinical surveillance systems. Using WBE, it is possible to carry out an early warning of a possible outbreak, monitor spatial and temporal trends of infectious diseases, produce real-time results and generate representative epidemiological information in a territory, especially in areas of social vulnerability. Despite the historical uses of this approach, particularly in the Global Polio Eradication Initiative, and for other pathogens, it was during the COVID-19 pandemic that occurred an exponential increase in environmental surveillance programs for SARS-CoV-2 in wastewater, with many experiences and developments in the field of public health using data for decision making and prioritizing actions to control the pandemic. In Latin America, WBE was applied in heterogeneous contexts and with emphasis on populations that present many socio-environmental inequalities, a condition shared by all Latin American countries. This manuscript addresses the concepts and applications of WBE in public health actions, as well as different experiences in Latin American countries, and discusses a model to implement this surveillance system at the local or national level. We emphasize the need to implement this sentinel surveillance system in countries that want to detect the early entry and spread of new pathogens and monitor outbreaks or epidemics of infectious agents in their territories as a complement of public health surveillance systems.

Smart Cities after COVID-19: Building a conceptual framework through a multidisciplinary perspective

This study provides theoretical grounds for planning smart cities using multidisciplinary approaches, offering insightful suggestions to researchers and policy- and decision-makers. Its main purpose is to contribute to the debate on the new connotations of the smart city paradigm in the context of the COVID-19 pandemic. It will emphasize how the Internet of Things and related technologies will collaborate to develop an antivirus-built environment against future pandemics. In this context, the study proposes a conceptual framework that provides a futuristic vision of prevention control, contingency planning, and measures against future risks. Although a smart city ecosystem improves citizens' lives, building it may involve design, implementation, and operational challenges that must be addressed.

Research on Architectural Planning and Landscape Design of Smart City Based on Computational Intelligence

City brain is a complex system, including online center, server network, and system with given algorithm. The core of the city brain is the intelligent system. After putting the urban brain into the intelligent nerve center, on the basis of not changing its original data structure, combining its own characteristics for design and then integrating into application, it can intelligently change the urban management mode. Urban planning leads the development of smart cities on a certain meaning, and smart city planning must have scientific and rational urban planning. The intelligent model is used to make urban planning form a more modern, convenient, and reasonable urban architectural planning. Some influential books on classical architectural theory are the theoretical basis of intelligent urban planning and even the trend and implementation blueprint of how smart cities will develop in the future. In this paper, four algorithms, ant colony algorithm, particle swarm optimization algorithm, genetic algorithm, and improved ant colony algorithm, are proposed to optimize the characteristics of urban architectural planning and landscape design; especially the security research of architecture and landscape characteristics is very important. The improved ant colony algorithm has the shortcoming of insufficient optimization ability in the face of complex path selection. By improving the influencing factors, a new ant colony algorithm is created. The improved ant colony algorithm achieves the best in security features, so it is advocated to use this algorithm for planning and design. The urban form in smart city aims to create a beautiful and comfortable urban environment, improve the competitiveness of cities in the rapid urbanization process, improve the living standards of the public, and shape the image of this beautiful city.

The Dual Role of Artificial Intelligence in Developing Smart Cities

Defining smart city pillars, and their nature and essence, continues to be debated in the scientific literature. The vast amount of information collected by electronic devices, often regarded merely as a means of rationalizing the use of resources and improving efficiency, could also be considered as a pillar. Information by itself cannot be deciphered or understood without analysis performed by algorithms based on Artificial Intelligence. Such analysis extracts new forms of knowledge in the shape of correlations and patterns used to support the decision-making processes associated with governance and, ultimately, to define new policies. Alongside information, energy plays a crucial role in smart cities as many activities that lead to growth in the economy and employment depend on this pillar. As a result, it is crucial to highlight the link between energy and the algorithms able to plan and forecast the energy consumption of smart cities. The result of this paper consists in the highlighting of how AI and information together can be legitimately considered foundational pillars of smart cities only when their real impact, or value, has been assessed. Furthermore, Artificial Intelligence can be deployed to support smart grids, electric vehicles, and smart buildings by providing techniques and methods to enhance their innovative value and measured efficiency.

The Path of Digital Government and University Asset Intelligence Value-Added Service Driven by Block Chain Technology

At present, the government and universities mainly adopted the centralized management mode in the sharing of asset digital resources, which not only had low work efficiency, but also cannot make full use of resources. Block chain technology played an important role in enterprise asset data resource data sharing, but there was less research on asset data sharing services applied to digital government and universities. Therefore, this paper proposed the research on the path of government and university asset value-added service driven by block chain technology, in order to provide reference for improving the utilization rate of state-owned assets and better meeting the needs of users. Based on the research on the problems existing in the digital resource management of relevant government agencies and university departments at home and abroad, combined with the application advantages of block chain technology in relevant fields, this paper analyzed the composition of block chain and the application technical characteristics of value-added services. Starting from the current situation of asset data information management by the government and universities and the needs of users for resources, this paper expounded the important impact of block chain technology on digital government and university asset information management. By analyzing the elements of block chain value-added service, this paper put forward the block chain-driven asset intelligent value-added service mode. In order to track and manage users’ transaction information and meet users’ demand for resources, the application path of asset intelligent value-added service driven by block chain technology was constructed based on the asset value-added service mode, and combined with the characteristics of users’ request for resources, the implementation method of asset intelligent value-added service path was proposed. The experimental results showed that the method proposed in this paper had more advantages than the existing data sharing service system, and can effectively provide technical support for asset value-added services.

IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies

In recent years, smart cities have been significantly developed and have greatly expanded their potential. In fact, novel advancements to the Internet of things (IoT) have paved the way for new possibilities, representing a set of key enabling technologies for smart cities and allowing the production and automation of innovative services and advanced applications for the different city stakeholders. This paper presents a review of the research literature on IoT-enabled smart cities, with the aim of highlighting the main trends and open challenges of adopting IoT technologies for the development of sustainable and efficient smart cities. This work first provides a survey on the key technologies proposed in the literature for the implementation of IoT frameworks, and then a review of the main smart city approaches and frameworks, based on classification into eight domains, which extends the traditional six domain classification that is typically adopted in most of the related works.

Modeling an Edge Computing Arithmetic Framework for IoT Environments

IoT environments are forecasted to grow exponentially in the coming years thanks to the recent advances in both edge computing and artificial intelligence. In this paper, a model of remote computing scheme is presented, where three layers of computing nodes are put in place in order to optimize the computing and forwarding tasks. In this sense, a generic layout has been designed so as to easily achieve communications among the diverse layers by means of simple arithmetic operations, which may result in saving resources in all nodes involved. Traffic forwarding is undertaken by means of forwarding tables within network devices, which need to be searched upon in order to find the proper destination, and that process may be resource-consuming as the number of entries in such tables grow. However, the arithmetic framework proposed may speed up the traffic forwarding decisions as relaying on integer divisions and modular arithmetic, which may result more straightforward. Furthermore, two diverse approaches have been proposed to formally describe such a design by means of coding with Spin/Promela, or otherwise, by using an algebraic approach with Algebra of Communicating Processes (ACP), resulting in a explosion state for the former and a specified and verified model in the latter.