Search Results (53)

Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. Collaborative training across multiple cooperative smart buildings enables model development without direct data sharing, ensuring privacy by design. Furthermore, the design of the proposed method considers three key principles: sustainability, adaptability, and trustworthiness. The proposed data pre-processing and engineering system significantly reduces the amount of data to be processed by the CNN, helping to limit the processing load and associated energy consumption towards more sustainable Artificial Intelligence (AI) techniques. Furthermore, the data engineering process, which includes sampling, feature extraction, and transformation of data into images, is designed considering its adaptability to integrate new sensor data and to fit seamlessly into a zero-touch system, following the principles of Machine Learning Operations (MLOps). The designed CNNs allow for the investigation of AI reasoning, implementing eXplainable AI (XAI) techniques such as the correlation map analyzed in this paper. Using the ToN-IoT dataset, the results show that the proposed FL-IDS achieves performance comparable to that of its centralized counterpart. To address the specific vulnerabilities of FL, a secure and robust aggregation method is introduced, making the system resistant to poisoning attacks from up to $20\%$ of the participating clients. Full article

Climate-smart agriculture (CSA) has emerged as a critical strategy to address the intertwined challenges of climate change, food insecurity, and environmental degradation, particularly among smallholder farmers in Southern Africa. This study reviews the existing literature on the adoption and scaling of CSA innovations among smallholder farmers in South Africa, focusing specifically on the roles played by institutional mechanisms, policy frameworks, and market dynamics. The findings reveal that while CSA interventions—such as conservation agriculture, drought-tolerant crop varieties, and precision irrigation—have demonstrated positive outcomes in enhancing productivity, food and nutritional security, and climate resilience, adoption remains uneven and limited. Key barriers include insecure land tenure, insufficient extension and climate information services, limited access to credit and inputs, and fragmented institutional support. The analysis highlights the importance of secure land rights, functional farmer cooperatives, effective NGO involvement, and inclusive governance structures in facilitating CSA adoption. Further, the review critiques the implementation gaps in South Africa’s climate and agricultural policy landscape, despite the existence of comprehensive strategies like the National Climate Change Response Policy and the Agricultural Policy Action Plan. This study concludes that scaling CSA among smallholder farmers requires a holistic, multi-level approach that strengthens institutional coordination, ensures policy coherence, improves market access, and empowers local actors. Targeted financial incentives, capacity-building programs, and value chain integration are essential to transform CSA from a conceptual framework into a practical, scalable solution for sustainable agricultural development in South Africa. Full article

Smart cities are confronted with a variety of disaster threats. Among them, natural fires pose a serious threat to human lives, the environment, and asset security. In view of the fact that existing research mostly focuses on the analysis of accident precursors, this paper proposes a dynamic risk-modeling method based on Stochastic Petri Nets (SPN) and Bayesian theory to deeply explore the evolution mechanism of urban natural fires. The SPN model is constructed through natural language processing techniques, which discretize the accident evolution process. Then, the Bayesian theory is introduced to dynamically update the model parameters, enabling the accurate assessment of key event nodes. The research results show that this method can effectively identify high-risk nodes in the evolution of fires. Their dynamic probabilities increase significantly over time, and key transition nodes have a remarkable impact on the emergency response efficiency. This method can increase the fire prevention and control efficiency by approximately 30% and reduce potential losses by more than 20%. The dynamic update mechanism significantly improves the accuracy of risk prediction by integrating real-time observation data and provides quantitative support for emergency decision making. It is recommended that urban management departments focus on strengthening the maintenance of facilities in high-risk areas (such as fire alarm systems and emergency passages), optimize cross-departmental cooperation processes, and build an intelligent monitoring and early-warning system to shorten the emergency response time. This study provides a new theoretical tool for urban fire risk management. In the future, it can be extended to other types of disasters to enhance the universality of the model. Full article

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change. Full article

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management. Full article

Population growth and environmental burden have turned the efforts of cities globally toward smarter and greener mobility. Cooperative and Connected Automated Mobility (CCAM) serves as a concept with the power and potential to help achieve these goals building on technological fields like Internet of Things, computer vision, and distributed computing. However, its implementation is hindered by various challenges covering technical parameters such as performance and reliability in tandem with other issues, such as safety, accountability, and trust. To overcome these issues, new distributed and decentralized approaches like blockchain and smart contracts are needed. This paper identifies a comprehensive inventory of CCAM challenges including technical, social, and ethical challenges. It then describes the most prominent methodologies using blockchain and smart contracts to address them. A comparative analysis of the findings follows, to draw useful conclusions and discuss future directions in CCAM and relevant blockchain applications. The paper contributes to intelligent transportation systems’ research by offering an integrated view of the difficulties in substantiating CCAM and providing insights on the most popular blockchain and smart contract technologies that tackle them. Full article

Background: Smart building, as an emerging building concept, has been a key driving force for the transformation and upgrading of the building industry; Methods: To better understand the latest research progress and trends in the field of smart building, this study uses CiteSpace 6.2.R4 bibliometric software to visualize, analyze, and interpret the literature related to the field of “Smart Building” in the WoS database from 2014 to 2023; Results: As a cross-sectoral and multidisciplinary field, smart building has received significant attention in recent years, with a rapid growth in the number of publications. International cooperation is strong, with China, the United States, and South Korea leading in the number of publications, but there is still room for enhanced collaboration among institutions. Keyword analysis shows that technology and humanized design are both crucial, and emerging technology has become the current research hotspot. Conclusions: The field of smart building has gained global attention, and more breakthroughs will be made in improving building efficiency, reducing energy consumption, and enhancing the user experience. This development is moving towards a smarter and more sustainable direction that will bring greater benefits to human life and the environment. Full article

The smart city concept has entered the public debate over the last decade as a concept for the development of urban space for the efficiency, improvement and availability of public and private services and sustainability. The Business Models Canvas is most often used in the literature for the creation of business models of smart services. On the basis of the above, we investigated whether the Business Models Canvas is the most used tool for creating business models for public smart services in Slovakia and whether cities and municipalities need to evaluate their models for the provision of public smart services. However, there is no commonly used methodology for evaluating smart city business models to help both practitioners and researchers choose the best option. The goal of the research is to create a tool for evaluating business models of public smart services in smart cities. The base method used was the Delphi method, based on the previous primary (content) analysis process of the Business Model Canvas best practices. In total, 709 towns and villages participated in the primary research. Subsequently, the obtained data were evaluated and used for further research using the Delphi method, in which 28 experts participated. The research was carried out between 2020 and 2023 in Slovakia. Primary research confirmed that the Business Models Canvas is the most used tool for creating business models for public smart services in Slovakia and cities and municipalities need to evaluate their models for the provision of public smart services. Areas and basic building blocks were also identified for the design of the evaluation methodology of business models for public smart services. The proposal of the methodology for evaluating business smodels for public smart services in Slovakia was implemented using the Delphi method with the cooperation of 28 experts. Based on the results of the Delphi method, a methodological procedure for evaluating business models for public smart services was established. The methodology proposed in the paper is a simple, organized, flexible and transparent system that facilitates the work of evaluators of business models of public smart services and marketing. Full article

Intelligent compaction (IC) has emerged as a breakthrough technology that utilizes advanced sensing, data transmission, and control systems to optimize asphalt pavement compaction quality and efficiency. However, accurate assessment of compaction status remains challenging under real construction conditions. This paper reviewed recent progress and applications of smart sensors and machine learning (ML) to address existing limitations in IC. The principles and components of various advanced sensors deployed in IC systems were introduced, including SmartRock, fiber Bragg grating, and integrated circuit piezoelectric acceleration sensors. Case studies on utilizing these sensors for particle behavior monitoring, strain measurement, and impact data collection were reviewed. Meanwhile, common ML algorithms including regression, classification, clustering, and artificial neural networks were discussed. Practical examples of applying ML to estimate mechanical properties, evaluate overall compaction quality, and predict soil firmness through supervised and unsupervised models were examined. Results indicated smart sensors have enhanced compaction monitoring capabilities but require robustness improvements. ML provides a data-driven approach to complement traditional empirical methods but necessitates extensive field validation. Potential integration with digital construction technologies such as building information modeling and augmented reality was also explored. In conclusion, leveraging emerging sensing and artificial intelligence presents opportunities to optimize the IC process and address key challenges. However, cooperation across disciplines will be vital to test and refine technologies under real-world conditions. This study serves to advance understanding and highlight priority areas for future research toward the realization of IC’s full potential. Full article

In this article, a distributed charging strategy problem for plug-in electric vehicles (PEVs) with feeder constraints based on generalized Nash equilibria (GNE) in a novel smart charging station (SCS) is investigated. The purpose is to coordinate the charging strategies of all PEVs in SCS to minimize the energy cost of SCS. Therefore, we build a non-cooperative game framework and propose a new price-driven charging control game by considering the overload constraint of the assigned feeder, where each PEV minimizes the fees it pays to satisfy its optimal charging strategy. On this basis, the existence of GNE is given. Furthermore, we employ a distributed algorithm based on forward–backward operator splitting methods to find the GNE. The effectiveness of the employed algorithm is verified by the final simulation results. Full article

As urbanization has accelerated, China has started to build smart cities, which have formed smart-city clusters. It is critical to coordinate development within smart-city clusters to enhance the efficiency of city-cluster construction. From the perspective of demographic economics, this study innovatively constructed an evaluation system for the coordinated development of smart-city clusters and utilized the coupled coordination degree model to conduct an in-depth study of smart-city clusters in Jiangsu Province. The results show that there are clear differences in the development between the three regions of Jiangsu Province: Southern Jiangsu, Central Jiangsu, and Northern Jiangsu. The development within Jiangsu Province is imbalanced, where the overall development trend is high in the southern region and low in the northern region. The main driving factors include geography, the Matthew effect, game thinking, and industrial structure. Accordingly, the results suggest the following recommendations for the coordinated development of smart-city clusters: strengthening cross-regional cooperation, promoting data sharing and interoperability, deepening synergistic industrial development, and expanding innovation capacity. Full article

This article takes a comprehensive look at the integration of mobile roaming, WiFi4EU and the smart city concept within the European Union in the context of sustainability. These initiatives form key elements of the digital development and transformation of European cities. Starting with a brief look at the functioning of the European Union’s internal market, the article briefly analyzes the objectives of these projects, highlighting their interplay and benefits for citizens. It focuses on the development of smart cities and the importance of digital connectivity in the process of building smart cities. It discusses the WiFi4EU initiative, which provides funding for free public WiFi networks and promotes digital inclusion. It also looks at the core pillars of smart cities, including digital connectivity, efficient transport, environmental protection, innovation and citizen participation. The article discusses the challenges associated with this integration, such as ensuring interoperability of different technological solutions and data privacy. It also highlights the importance of cooperation between city authorities, local communities and European institutions to achieve successful digital urban development. The research emphasizes the economic sustainability implications of these integrated technologies, considering the potential for innovation, job creation and economic growth within the digital and tech sectors. The main method used in the writing process was the analysis method, which was complemented by the comparison and synthesis methods. The final discussion assesses the benefits and challenges that this integration brings for the development of cities and the improvement of the quality of life of citizens. By critically examining the convergence of mobile roaming, WiFi4EU and smart cities in the European Union, this study aims to provide insights into the transformative potential of sustainable connectivity. The findings contribute to ongoing discussions on urban development strategies, emphasizing the need for a holistic approach that addresses both technological advancements and the imperative of sustainable practices for the benefit of current and future generations. Full article

Green technological innovation in the prefabricated building supply chain (PBSC) is an important way to realize the sustainable development of the construction industry. However, the competitive environment and the green input costs reduce the willingness of PBSC firms to improve the green technology level. This paper constructs a PBSC consisting of a smart construction management platform (SCMP), a contractor, and prefabricated-component manufacturers (PCMs) to explore green cooperation strategies in the PBSC. Stackelberg game models are constructed and the green technology level and PBSC profit under different cooperation strategies are examined. The study shows that the optimal service commission of the SCMP increases with the cost parameter of green technology and the intensity of competition between PCMs. However, the green technology level decreases with the competition. The integration strategy does not necessarily achieve the highest level of green technology. The horizontal cooperation among competing PCMs is not conducive to improving the green technology level, but PCMs always have incentives to form horizontal cooperative alliances to achieve Pareto improvement under certain conditions. For the SCMP, the vertical cooperation strategy with PCMs is the most favorable, but for the PBSC, the system profit under the integration strategy is the most profitable. This study enriches the theoretical foundation of the PBSC and provides theoretical guidance for green cooperation strategies in PBSCs. Full article

Gulf Cooperation Council (GCC) members have experienced tremendous transformation in their smart cities in recent years. Every GCC nation has its own urban planning authority to manage urban growth and development since its independence from British rule. These planning agencies create strategies, plans, rules, and oversee the building process. The novelty of this research lies in its comprehensive analysis, cross-border comparisons, and the generation of insights that contribute to a deeper understanding of urban planning dynamics and strategies within the Gulf Cooperation Council. The objective of this article is to examine the national spatial strategies (NSS) and vision plans of GCC countries. This study discusses the existing methods, plans, and efforts to accomplish this goal while emphasizing prospective opportunities, problems, and difficulties. We used the SWOT method to evaluate the national vision plans of GCC countries. The analysis identifies successful outcomes that the GCC countries have already achieved in terms of their national spatial strategies. The weakness in the current strategies is oil dependency, which could be strengthened. Potential opportunities in the tourism sector need to be taken full advantage of, and potential threats, like regional tension, need to be managed to prevent the failure of the development of the existing urban system. The primary suggestion entails executing programs outlined by the respective line ministries and ensuring efficient management of urban spatial expansion by municipal authorities. However, the findings could serve as lessons for those at the helm of affairs in GCC countries to collaborate and achieve a comprehensive national strategic plan. Full article

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field. Full article