Intelligent Road Maintenance

Prediction model optimization of gas turbine remaining useful life based on transfer learning and simultaneous distillation pruning algorithm

The conceptual fusion of smart city and sustainability indicators has inspired the emergence of the smart sustainable city (SSC). Given the early stage of development in this field, most SSC studies have been primarily theoretical. Notably, existing empirical studies have overlooked the crucial aspect of feature engineering in the context of SSC, despite its significance in advancing SSC initiatives. This paper introduces an approach advocating for feature subset selection to maximize prediction accuracy and minimize computational time across diverse SSC indicators encompassing socio-cultural, economic, environmental, and governance categories. The study systematically collected multiple datasets on SSC indicators, covering various themes within the SSC framework. Employing six carefully chosen multiple-objective evolutionary feature selection algorithms, the research selected feature subsets. These subsets were then utilized in modeling algorithms to predict SSC indicators. The proposal enhanced prediction accuracy for life expectancy, online shopping intentions, energy consumption, air quality, water quality, and traffic flow for a smart and sustainable city by minimizing the subset features. The findings underscore the efficacy of feature subset selection in generating minimal features, thereby enhancing both prediction accuracy and computational efficiency in the realm of SSC indicators. For researchers aiming to develop sustainable systems for real-time data monitoring within SSC, the identified subset features offer a valuable resource, negating the necessity for extensive dataset collection. The provided SSC datasets are anticipated to serve as a catalyst, inspiring researchers to embark on empirical studies that explore SSC development from diverse perspectives, ultimately contributing to a more profound understanding of the SSC dynamics.

In 2015, the United Nations General Assembly established a total of seventeen Sustainable Development Goals (SDGs). This paper focuses on SDG-7: "Affordable and Clean Energy". The aim of SDG-7 is to guarantee individuals have access to inexpensive, sustainable, consistent, and modern energy. The UN aims to enhance sustainability through the global sharing of renewable energy, achieved by a diversified energy mix, by 2030. At the same time, ongoing urbanization, modern lifestyle, and the growth of industries increase the demand for energy in smart cities, necessitating the development of a decentralized energy system for sustainability. This paper proposes a customizable Solar PV system integrated with an efficient DC-DC converter, a powerful battery system, and a PSO-based energy distributor to enhance energy sustainability in a smart city without any loss. Compared to earlier works, this storage, and optimal energy distribution to enhance sustainability in smart cities. An intelligent energy management technique is developed to ensure a stable energy flow among these processes. Optimization techniques are applied in conversion, storage, and distribution modules to reduce energy loss and increase utilization. The overall circuit model is simulated in MATLAB software, and the output is verified. From the production, it is evident that the proposed model has decreased grid dependency by over 60% while maintaining system stability and energy resilience. This paper provides a pathway to create a real-time RES to increase sustainability in a smart city as well as to fulfil SDG-7 by 2030.

According to United Nations estimates, about 70% of the world’s population will live in urban areas by 2050. (https://www.un.org) Smart sustainable cities technologies and approaches, including those based on information and communications technology (ICT) solutions and Internet of things (IoT), are already helping people to overcome multiple challenges of urban development and to progress towards achieving global objectives for cities, particularly Sustainable Development Goal 11 ‘Make cities and human settlements inclusive, safe, resilient and sustainable’ (The first United Smart Cities SMART CITY LAB opened in Vienna, supporting co-creation of solutions for smart sustainable cities worldwide 2018, [1]). The Smart Cities Mission (SCM), launched in 2015, by the Government of India, aims at building up sustainable and inclusive cities that provide core infrastructure, a clean and sustainable environment and a decent quality of life to its citizens. The strategic components of the Mission are city improvement (retrofitting), city renewal (redevelopment) and city extension (greenfield development) with smart solutions applied in service delivery and governance (SDG India Index & Dashboard 2019–2020, [2]). There are several urbanization models that incorporate digital technologies to address some of the urbanization and sustainability challenges: Digital cities feature the integration of digital technology into the city’s core infrastructure systems; intelligent cities rely on the digital city infrastructure to build intelligent buildings, transportation systems, schools, enterprises, public spaces, public services, etc., and to integrate them into intelligent urban systems; and smart cities—deploy intelligent urban systems at the service of socio-economic development and improving urban quality of life (Estevez et al, Smart sustainable cities, reconnaissance study 2016, [3]) So, here, we are trying to deal with systems of systems in physical ecosystems powered by various ICT and digital technologies. Meanwhile, the existing technological approaches tend to latch onto the most hyped technology of the year and produce many silo-ed solutions for smart cities which, although elevates technology innovation, fails to engage its citizens. Therefore, a design intervention is needed to help bridge this and make technologies work for humans, the citizens and the primary stakeholder. This interpretation paper is an inquisition into the role of design and designers, to bring the right kind of approach when it comes to sustainable smart city design. The paper first lays down the high-level role and responsibilities of designers and its various other key stakeholders in crafting a smart city. Then, it talks about the right kind of methodologies that designers bring at all levels of sustainable smart city design—from working with official bodies in co-creation and collaboration mode to the end-users—the citizens—to understand their wants, needs and desires. The paper then details out a case study of a smart campus where the aforementioned methods are applied in order to come up with the most effective designs for the smart city that meet the official bodies’ KPI requirements and at the same time, delivers best-in-class experiences to citizens.

This paper focuses on the challenges and opportunities of achieving sustainability in smart cities. It depends on maintaining the balance between energy efficiency and connectivity through energy-efficient protocols. The study surveys systems with energy-efficient protocols, emphasizing their strengths and limitations in maintaining sustainability in smart cities. The need for energy-efficient protocols in smart city settings is that these protocols dynamically adjust operations to optimize energy consumption within sensor nodes in communication networks while considering parameters: like heterogeneity, traffic patterns, and environmental factors. Evaluation against key performance metrics such as energy consumption, data delivery ratio, network lifetime, and throughput identify promising technologies to achieve an optimal balance between connectivity and energy efficiency in smart cities. It concludes by proposing future research directions and considerations to make the development of sustainable networking solutions rapidly tailored to the evolving landscape of smart cities. This paper aims to present a comprehensive overview of the pivotal role played by energy-efficient protocols in fostering sustainable networking architectures.

Building a smart city that follows sustainability goals enhances the quality of life and preserves environmental, human, and social capital. Yet, existing smart sustainable city projects have concentrated on the technological dimensions of smart cities such as using big data or smart devices to follow sustainability goals. Currently, there is no comprehensive category of smart sustainable city indicators in the literature. This paper aims to discover these indicators by considering the common features of sustainability and smart city concepts. Two rounds of the content analysis technique were employed to investigate semantic, lexical, and conceptual relationships between smart city and sustainability indicators. This paper employed the Sustainable Development Indicators suggested by OECD and the Smart City Index Master by Cohen as the two main groups of indicators. The findings offer a novel set of indicators that enables policymakers and researchers to consider the smartness and sustainability of their projects simultaneously. This includes socio-cultural, economic, environmental, and governance categories with 28 associated indicators. The outcome of this paper offers a unique combined category of smart sustainable city indicators by considering the key elements of sustainability and smart city concepts. Academics and policymakers can also employ this set of indicators as a guideline to build a smart sustainable community.

The evolution of smart cities represents a pivotal transformation in urban development, driven by the integration of cutting-edge technologies. Among these, blockchain and edge intelligence have emerged as pivotal forces shaping the future of smart cities. This paper presents a comprehensive review and outlook on the potential synergy between blockchain and edge intelligence, highlighting their transformative impact on sustainable smart city development. In our analysis, we delve into the key components and technologies associated with smart cities, emphasizing their goals of sustainability, efficiency, and improved quality of life. We introduce the concepts of blockchain and edge intelligence, elucidating their applications across various industries and urban domains. Moreover, we identify gaps in the existing literature and underscore the critical need for further research in the synergy of these technologies in smart cities. Our exploration extends to the significance of the study, emphasizing the timeliness of this research amidst growing interest in sustainable smart cities. We discuss the potential benefits and implications of this technological convergence for urban planning, technology adoption, and sustainability. This paper envisions smart cities that prioritize sustainability, circular economies, and data privacy, while fostering innovation and collaboration among public and private stakeholders. As we look to the future, we anticipate that this convergence will pave the way for more resilient, sustainable, and inclusive smart cities, and we outline potential areas for further research and development in this exciting field.

South Korea has a long history of the planning, development, and management of smart cities to integrate emerging technological advances into complex physical infrastructure. This article explores lessons learned from smart city programs in South Korea to better understand the challenges and opportunities of future sustainable smart city innovation and development. This article conducted a comprehensive review and analysis of South Korea's smart city programs and conceptualized a sustainable smart city framework that will assist policymakers, planners, citizens, and other key stakeholders. This research proposed Governance, Policy, and Services (GPS) as the three pillars of a successful smart city framework, in addition to integrating physical and cyber infrastructures. This article argues that a smart city should function as a service platform that incubates and delivers long-term services to citizens and society. This article also emphasizes that a strong groundwork of the GPS framework will lead to the successful adaptation of innovative technologies and ideas for future smart city programs.

PurposeTechnologies continue to disrupt logistics and freight transport (known as smart logistics), but their impacts on smart city sustainability is underinvestigated. Drawing on technology, organisation and environment (TOE) perspective, the objective of this study is to empirically investigate the hierarchical effects of smart logistics on smart city sustainable dimensions (i.e. environmental, social and economic).Design/methodology/approachThe study used cross-sectional survey to collect data from urban transporters, warehouse managers, retailers and information technology (IT) managers in Australia. Data were analysed using structural equation modeling (SEM) to test the hypothesised relationship between constructs of smart logistics and smart city sustainable performance.FindingsThe findings reveal that information and communications technologies (ICTs) use and IT capability (ITC) have positive and significant effects on smart logistics. Technology-enabled smart logistics have an immediate positive effect on smart city environment, which in turn has positive impacts on social and economic performance.Practical implicationsThe study informs managers that smart logistics equipped with freight transport telematics can improve smart city environment through enhanced tracking and tracing of goods movement. The improved environmental stewardship is likely to support social and economic performance.Originality/valueSmart city research remains primarily theoretical and focussed on concerns surrounding sustainable growth amid urbanisation and digitalisation. City logistics and urban freights play key role in smart city economic growth, but vehicular pollution pose social and environmental challenges. Technology-assisted smart logistics are likely to improve smart city sustainable performance but yet to find how they affect each other.

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

A rising number of people agree that the actions conducted in India as part of the Smart Cities project should be utilized to create models for environmentally sustainable smart cities. The circumstances in developing nations differ from those in industrialized nations, which have historically worked toward sustainable cities and are currently working toward smart sustainable cities. The Smart cities standards as they stand now seem to place more of an emphasis on governance challenges, social and economic development and the use of contemporary information and communication technology (ICT). Background: Sustainable indicators are the measures that are used to gauge the effectiveness of environmental strategies. These activities are connected to certain goals and outlined in a sustainability plan. Reducing waste during production, for instance or the carbon footprint. Their execution serves as a gauge for whether things are moving in the proper way. Methods: This study emphasizes on 20 top smart cities of India using GIS Mapping techniques and geospatial data science. Results: These indicators are often used to assess if the city of India is accomplishing its goals. If there is a deviation, remedial action may be taken. Sustainability indicators, then, assess both the performance of the city’s growth and its plan execution. Conclusions: But it's crucial to choose the appropriate parameters and make sure they're directly related to the suggested goals. If not, they will basically stop working.

Smart cities rely on innovative technologies, guidelines, and mechanisms to advance city dwellers’ quality of life (QoL). Notwithstanding the global attention the smart city concept has received within the last decade, studies establishing pragmatic approaches for implementing smart sustainable city strategies in the Gulf region are rare. This study modelled a practical framework for implementing smart sustainable city strategies and megaprojects in one of the Gulf cities. A qualitative research methodology was used to assess smart city strategies from four cities to identify the optimum implementation strategies. This study design framework adopted a case study methodology, the identification of knowledge gaps, data collection and analysis, and interpretations of key findings. Best practices, paramount/relevant stakeholders, main issues/relevant considerations, 15 key performance indicators (KPI), and outputs/deliverables involved in diverse smart sustainable city strategies and project lifecycle phases were identified. The lifecycle phases adopted in the study were (i) conceptualization, (ii) planning/design, and (iii) installation/closure. A pragmatic understanding of how to effectively appraise, monitor, and implement smart sustainable city strategies and megaprojects is provided for policy/decision-makers and built environment experts in Saudi Arabia and globally. The proposed implementation strategic framework can perform the function of an appraisal tool for assessing each phase of the smart city project’s life cycle progress, informing preventions of delays or implementation challenges. This study’s contribution to research knowledge is the development of a model that reveals and illustrates the connections between different phases of smart sustainable city strategies and projects.

Smart cities are aimed at connecting urban infrastructures to enhance the efficiency of their operation and services while taking sustainability goals into consideration. As a result of the intermittency associated with renewable generation, smart city systems such as smart grids and microgrids may not be able to ensure the security of supply. This can be mitigated by allowing these systems to trade surplus energy with other neighboring systems through local energy markets based on peer-to-peer schemes. Such an approach can play an important role on achieving sustainability due to the positive impacts at the economic, social, and environmental level. Therefore, this work explores the design of local energy markets to help determine how they are relevant to smart grid and microgrid applications and what their contributions are to sustainability in smart cities. Essentially, this is achieved by performing a literature review to address key characteristics related to the design of local energy markets while considering their relationship with urban sustainability. In addition, the concept of game theory and its potential to evaluate market designs are also introduced and discussed. Finally, the suitability of centralized, decentralized, and distributed market designs for each dimension of sustainability is estimated based on their design characteristics.

This systematic review has demonstrated that Artificial Intelligence (AI) plays a transformative role in Smart Building Management Systems (SBMS), enhancing energy efficiency, predictive maintenance, and sustainable automation. By analyzing 472 high-quality studies, this research has identified that AI-driven HVAC optimization, lighting control, solar energy forecasting, and demand-side energy management significantly reduce energy consumption, with reported efficiency improvements ranging between 20-50%. The review also highlights that reinforcement learning (RL) and deep learning (DL) models outperform traditional rule-based systems by dynamically adjusting building operations based on real-time sensor data, occupancy patterns, and environmental conditions. AI-powered fault detection and predictive maintenance further improve building operations by reducing unexpected system failures, lowering maintenance costs by up to 35%, and extending equipment lifespan. Moreover, the study underscores the growing potential of hybrid AI models integrating IoT, blockchain, and cloud computing in enabling real-time energy monitoring, decentralized energy trading, and secure automation. Despite these advancements, the review also reveals critical research gaps, particularly the lack of large-scale empirical validation, challenges in AI scalability, and the need for interdisciplinary collaboration to enhance AI’s effectiveness in sustainable building design. While theoretical and simulation-based studies provide strong evidence of AI’s benefits, real-world pilot projects, regulatory frameworks, and cross-sector collaborations are essential for AI-driven smart building technologies to achieve widespread adoption. Addressing these challenges through industry-academia partnerships, policy support, and further longitudinal research will be key to ensuring that AI-powered SBMS can drive long-term sustainability, operational efficiency, and energy resilience in modern smart infrastructure.