Aug 2026· Advanced Electromagnetics· Vol 15, pp. 6802-6807· 0 citations· 11 references
TL;DR
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.
Abstract
The increasing integration of IoT sensors, fiber-optic sensing and wireless communication links into urban architecture has made smart city infrastructure more vulnerable to disruptions as these complex, dynamic operating environments grow increasingly diverse with connectivity. Traditional static management frameworks cannot solve the problem of real-time perception and comprehensive planning of cross-system hazards. This paper proposes a dynamic monitoring and emergency dispatch model based on high-precision spatio-temporal big data. A data twin view of an integrated system based on heterogeneous data in the form of IoT sensing, remote sensing, GIS, and social sensing is established with a common spatiotemporal reference point. Accordingly, multi-layer complex networks are used to describe infrastructure dependencies, and spatiotemporal graph convolutional networks are integrated to obtain dynamic risk propagation simulations. In addition, a better non-dominated sorting genetic algorithm is adopted for multi-objective adaptive emergency resource scheduling. The results show that the model’s anomaly identification accuracy is 95.3, risk propagation prediction accuracy is 88.7, and only the time to generate the schedule is 4.2 seconds. The proposed data fusion and scheduling logic is consistent with smart-city systems that rely on wireless sensing, remote sensing and communication infrastructure.
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
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
Smart city transport networks must be highly adaptive, meaning they can quickly adjust to new road conditions. This research aims to provide a smart city architecture that can detect accidents and track traffic in realtime using edge-cloud computing, deep learning-based video analytics, and IoT sensing. In order to correctly analyse traffic and detect accidents, the platform continuously gathers heterogeneous data from roadside cameras and automobile sensors, performs essential analytics at the edge to decrease latency, and runs robust cloud analytics. The software is able to do precise traffic analyses and detect accidents because of this. Abnormal traffic event spatial and temporal patterns are captured using a mixed deep learning architecture employing recurrent neural networks and convolutional neural networks. Also, for proactive traffic management, a module that forecasts traffic patterns can be used. The proposed system exhibits low response time, robustness under varying traffic and lighting conditions, and outstanding detection accuracy, according to the experimental results. The system is both scalable and inexpensive, and it improves urban mobility, response times to emergencies, and road safety.
R. Elankavi, Imran Alam, Mogadala Mounika et al.· ITM Web of Conferences· 0 citations
Rapid urbanization has created a dual challenge for green city development: environmental monitoring efficiency often lags behind ecological demand, while landscape design still relies heavily on static surveys and designer experience. Integrating communication technology with IoT sensing can address these issues by enabling continuous environmental perception and data-driven landscape optimization. This study constructs a real-time urban environmental monitoring system comprising 120 composite sensor nodes deployed across four functional zones: commercial districts, residential areas, industrial buffer zones, and urban green spaces. A dual-channel communication architecture combining NB-IoT and LoRaWAN, supported by an edge-cloud collaborative computing framework, is used to monitor atmospheric quality, thermal environment, acoustic conditions, and light environment. The wireless communication design also provides a practical reference for low-power sensor networking and propagation-aware deployment in dense urban environments. Results show that overall data integrity reached 99.1%, and edge computing reduced end-to-end response latency by 73.6%. Using multidimensional monitoring datasets as a decision basis, landscape spatial layout optimization reduced annual mean PM2.5 concentrations by 26.3% in targeted zones and lowered local urban heat island intensity by 41.7% relative to control areas. Resident satisfaction increased by 19.4 points, and AHP evaluation scores increased by 39.5%, validating a closed-loop perception-transmission-analysis-design paradigm for green urban landscape optimization.
With the rapid development of Internet of Things (IoT) technologies and intelligent building systems, traditional fire safety management approaches face challenges in real-time perception, dynamic risk assessment, and adaptive emergency response. This study proposes a digital twin-based intelligent fire safety management system for smart buildings by integrating IoT-enabled mechanical monitoring and dynamic evacuation optimization. A multi-layer digital twin architecture is developed to establish a real-time connection between physical building environments and virtual models, enabling continuous monitoring of thermal conditions, smoke propagation, ventilation performance, and fire protection equipment status. IoT sensor networks are employed to collect real-time environmental and mechanical system data, while data-driven prediction models are applied to identify early fire risks and estimate fire evolution patterns. Furthermore, a dynamic evacuation optimization strategy is developed by considering fire development, occupant distribution, smoke diffusion, and building mechanical conditions. The proposed framework enables adaptive decision-making for emergency evacuation and intelligent control of building safety systems. Simulation-based experiments demonstrate that the digital twin-driven approach can improve fire risk detection accuracy, reduce evacuation time, and enhance the resilience of smart building safety management compared with conventional static evacuation strategies. The proposed system provides an effective solution for next-generation intelligent fire protection by combining digital twin technology, IoT monitoring, mechanical system control, and AI-assisted emergency management.
Yi-Han Zhu, Wan-Ying Ren· Advances in Modern Biomedici...· 0 citations
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