Real-Time Disaster Prediction with Mobile Sensor Networks

Abstract

Natural disasters, such as earthquakes, floods, wildfires, and hurricanes, are occurring more frequently and becoming more severe, underscoring the need for advanced real-time prediction systems. Existing static monitoring infrastructures with limited coverage suffer from delayed processing and expensive maintenance costs. In comparison, mobile sensor networks (MSNs) offer cost-effective, dynamic, and scalable solutions for collecting real-time environmental data. This research focuses on the design and implementation of mobile sensor networks for real-time disaster prediction, with a particular emphasis on advanced sensing technologies and predictive algorithms. The methodology involves the deployment of mobile sensor units equipped with environmental sensors, communication modules, and data processors to monitor temperature, humidity, seismic activity, and air quality. These data are analyzed in real-time with machine learning algorithms to detect early warning signs of potential disasters and are centralized for further processing. System evaluation is performed through simulating disaster scenarios and is benchmarked against conventional fixed-network methodologies. Results indicate marked improvements in prediction accuracy, response time, and adaptability to rapidly changing conditions. This work analyzes the practical applications of MSNs in disaster-prone areas, discusses challenges such as energy consumption and data protection, and provides suggestions for future improvements. This study extends the expanding area of intelligent disaster management and mobile sensor networks to enhance early warning and response systems.