IoT Sensor Network for Urban Infrastructure Disaster Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current IoT systems face challenges in effectively monitoring and managing urban infrastructure health states and predicting disaster occurrences, leading to potential accidents and damages due to inadequate early detection and response mechanisms.
Innovation Solution
A method and apparatus utilizing IoT devices to collect and analyze sensing data from urban infrastructure, classify structures, generate standard models for device installation, calculate correlations, and transmit warning messages, enabling efficient data management and remote control to predict and respond to disasters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IoT devices are installed in urban infrastructures to monitor health states, then the ability to detect defects and predict disasters is improved, but the complexity of the management system increases
Solution Approach 1:
The patent segments the urban infrastructure management system into multiple independent IoT devices, each responsible for specific sensing functions (vibration, temperature, humidity sensors). These devices are distributed across different infrastructure locations and can independently collect and transmit data, reducing the complexity of centralized monitoring while improving defect detection coverage
Solution Approach 2:
The IoT devices are designed with multi-functionality, integrating multiple sensing capabilities (vibration sensors, temperature sensors, humidity sensors) and communication functions into single units. This universal design reduces the overall number of devices needed in the system while maintaining comprehensive monitoring capabilities across different infrastructure types
2Measurement precision
If multiple IoT devices are installed to collect comprehensive sensing data, then the accuracy of disaster prediction is improved, but the amount of data to be processed increases
Solution Approach 1:
The gateway extracts and filters critical information from the raw sensing data collected by multiple IoT devices. It identifies and processes only the most relevant parameters (vibration patterns, temperature thresholds, humidity levels) while discarding redundant data, thereby maintaining high prediction accuracy with reduced data processing requirements
Solution Approach 2:
The system implements selective data collection and processing, focusing on specific critical parameters and time periods most relevant to disaster prediction. Rather than processing all data from all devices continuously, the system activates intensive monitoring only when threshold values are approached or anomalies are detected, reducing overall data volume while maintaining prediction accuracy
3Loss of time
If sensing data is collected and analyzed in real-time to predict disasters, then the response time to potential disasters is improved, but the energy consumption of the system increases
Solution Approach 1:
The system implements periodic monitoring with variable intervals based on risk levels. During normal conditions, IoT devices and the gateway perform data collection and analysis at extended intervals, reducing energy consumption. When threshold values are approached or anomalies detected, the system automatically increases monitoring frequency to provide rapid disaster prediction and response, thus balancing energy usage with response time requirements
Data Source
AI summary
A method and an apparatus for urban infrastructure management using IoT. The method includes receiving sensing data from a plurality of IoT devices installed in the urban infrastructures; determining health states of the urban infrastructures or predicting occurrence of a disaster based on the received sensing data; and transmitting a warning message according to the predicted disaster occurrence to outside. Thus, the urban infrastructures can be managed efficiently, and the disaster occurrence can be predicted accurately.