Aug 2026· International Conference on Industrial IoT, Big Data, and Smart Cities· Vol 14325, pp. 143251C - 143251C-10· 0 citations
Engineering
TL;DR
This study explores and practices intelligent monitoring technology for transportation infrastructure slopes, providing theoretical support and practical experience for its large-scale application.
Abstract
With the development of smart cities, the intelligentization of transportation infrastructure such as highways and highspeed railways connecting smart cities has become an inevitable requirement. However, influenced by factors such as geological conditions, precipitation, and construction techniques, slope collapses and landslides pose significant risks to urban and transportation infrastructure safety. To effectively prevent these risks from escalating into disasters that cause severe losses to people's lives and property, traditional measurement methods struggle to meet the operational requirements of infrastructure for "real-time perception, intelligent analysis, and rapid assessment." Based on new technologies like 5G, the Internet of Things, and measurement robots, intelligent monitoring technology has emerged as a novel solution for safety risk prevention. This technology integrates functions such as data collection, data transmission, data calculation and analysis, assessment, and forecasting, enabling intelligent prevention of risks throughout the entire lifecycle of slopes. Grounded in the context of smart city development, this study explores and practices intelligent monitoring technology for transportation infrastructure slopes, providing theoretical support and practical experience for its large-scale application.
An intelligent monitoring system for roads and bridges based on Internet of Things technology that provides an intelligent and scalable technical solution for the full life-cycle management of roads and bridges, applicable to the regular monitoring and emergency response of large-scale transportation infrastructure such as expressway bridges, urban overpasses, and long tunnels.
The fast pace of urbanization has made smart and sustainable infrastructure management more important than ever. Because of their inherent silos, traditional urban management systems are unable to adapt in real-time to shifting demands in areas such as water distribution, public safety, energy consumption, traffic flow, and energy consumption. This study found that smart cities may use AI and the internet of things to adapt and manage their infrastructure using data. Sensors throughout the city’s infrastructure for transportation, power, buildings, and the environment provide data into Internet of Things devices. Analytics systems powered by AI can automate decision-making, enhance resource allocation, discover anomalies, and forecast demand using massive amounts of data. For predictive maintenance and real-time monitoring, the framework places an emphasis on interoperability, scalability, cybersecurity, and sustainability. By replacing reactive systems with proactive ones, adaptive algorithms and machine learning models can increase dependability, save costs, and revolutionize urban planning. Topics covered in the research include data privacy, infrastructure integration, and data governance. The convergence of AI with the Internet of Things (IoT) creates robust, efficient, citizen-centric urban ecosystems, as shown by comprehensive design and performance evaluation metrics. Smart cities that can adjust to changes in the environment, population, and economy are made possible by these discoveries.
P. Kumaresan, Hayel Khafajeh, R. Latha et al.· International Conference on...· 0 citations
Rapid urbanization has intensified pressure on energy systems, transportation networks, water resources, waste-management infrastructure, public health services, and the urban environment. Conventional city-management models, which often rely on fragmented information and reactive decision-making, are increasingly inadequate to address these interconnected challenges. Artificial Intelligence (AI) and the Internet of Things (IoT) provide a technological foundation for a transition from conventional urban administration toward intelligent, adaptive, and sustainability-oriented city management. IoT infrastructures enable continuous sensing and communication across physical urban environments, whereas AI converts large volumes of heterogeneous sensor data into predictions, classifications, recommendations, and automated decisions. This paper examines the integrated role of AI and IoT in sustainable smart city development through a conceptual and interdisciplinary review of research on smart urban systems, data analytics, edge computing, intelligent transportation, energy management, environmental monitoring, waste management, water conservation, public safety, and urban governance. The paper proposes an AI–IoT Closed-Loop Sustainable Urban Intelligence Framework consisting of sensing, connectivity, edge/cloud processing, artificial intelligence, decision-making and actuation, and sustainability evaluation layers. The analysis demonstrates that the value of AI–IoT integration lies not simply in increasing technological sophistication but in enabling cities to minimize resource consumption, anticipate infrastructure failure, reduce emissions, improve service responsiveness, and make urban systems increasingly adaptive. At the same time, cybersecurity vulnerabilities, privacy risks, algorithmic bias, interoperability problems, digital inequality, high infrastructure costs, and the environmental footprint of computing can weaken sustainability outcomes. The study therefore argues for a human-centered, secure, interoperable, transparent, and sustainability-measured model of smart city development. Future smart cities should be evaluated not by the quantity of connected devices deployed but by measurable improvements in environmental quality, resource efficiency, social inclusion, resilience, and quality of urban life.
P. S., Shaik Rahamtula, S. J et al.· Stanzaleaf International Jou...· 0 citations
The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Grace Ndlovu· International Journal of Mod...· 0 citations
Reliable monitoring and forecasting of large-scale environmental hazards require efficient sensing, robust information transmission, and intelligent analysis of heterogeneous spatiotemporal data. This study proposes a smart ice-prevention framework for the Yellow River based on distributed sensing networks, digital twin technology, and multi-source information fusion. A hierarchical architecture integrating monitoring and perception networks, communication infrastructure, cloud-based computing resources, and digital twin platforms is developed to support real-time acquisition and management of hydrological, meteorological, and ice-condition information. To improve situational awareness and forecasting capability, heterogeneous data from ground sensors, video monitoring systems, unmanned aerial vehicles, and remote-sensing platforms are fused through a multidimensional spatiotemporal data model. Machine-learning-based prediction models, pattern-recognition algorithms, and knowledge-driven reasoning mechanisms are further employed to achieve ice-condition forecasting, early warning generation, and emergency-response support. Experimental deployment demonstrates that the proposed framework significantly enhances monitoring efficiency, forecasting accuracy, and decision-support capability for ice-prevention operations. By integrating distributed sensing, spatiotemporal information fusion, digital twin modeling, and intelligent forecasting, the proposed framework provides an effective methodology for large-scale monitoring systems, environmental sensing networks, and data-driven hazard management in complex dynamic environments.
W. Du, L.-L. Li· Advanced Electromagnetics· 0 citations
A dynamic monitoring and emergency dispatch model based on high-precision spatio-temporal big data based on a better non-dominated sorting genetic algorithm is adopted for multi-objective adaptive emergency resource scheduling.
