Wireless Sensor Networks for Disaster Management and Emergency Response using SVM Classifier

Wireless Sensor Networks (WSNs) are essential for IoT-enabled crisis management and executive response. This paper explores WSNs' function in catastrophe situations and proposes new ways to improve emergency response operations. The article begins with a discussion of disaster management and emergency response scenarios and the necessity for real-time data collecting, monitoring, and analysis. WSNs are suitable for such applications due to their dense and dispersed sensing, tolerance to hostile conditions, and low power consumption. The research provides unique WSN deployment, data aggregation, and resource optimization methodologies for disaster management. These solutions will improve network coverage, dependability, and sensor longevity. The research also examines the synchronization of AI and human brainpower calculations with WSNs to enable smart navigation and robotized crisis response. The research study addresses WSN security and privacy in disaster management. It identifies flaws and suggests ways to classify, verify, and share data. A reenacted fiasco is used to evaluate the proposed alternatives. Situational awareness, response coordination, and emergency operations efficiency increase with the provided techniques. This study sheds light on WSNs' application in IoT-enabled emergency response and disaster management. The proposed solutions advance WSN technology and enable more effective and efficient emergency response systems.