A Data-Intelligent Scheme Toward Smart Rescue and Micro-Services

Abstract

A considerable portion of the world frequently experiences flooding during the monsoon season. As a result of this catastrophic event, hundreds of individuals have become homeless. In addition, rescuers are not usually effective enough to rescue the majority of victims. This is due to inadequate rescue operations infrastructure, a severe flaw in today's technologically advanced society. This manuscript proposes a microservice-dependent secure rescue framework that uses geographic information system mapping with a K-Means clustering algorithm to identify flood-prone regions. Numerous microservices, such as fleet management, cloud computing, and data security, integrate and execute the framework in preand post-flood situations. Labeling data from the proposed framework generates a support vector machinebased classifier for predicting flood risk. Furthermore, a hybrid A* algorithm is developed to find an optimal route for the rescue operation. Based on the K-means clustering results, which reduced the variance by 89.2 percent overall, dividing the data into six clusters was the best option for this study. The smoothness of the suggested hybrid algorithm is also used to verify its superiority.

WhatsApp-Based Cloud Service Chatbot Application for Emergencies or Disasters

“…Zaman et al [18] proposed a microservice-centric safe rescue architecture where integrates a geographic information system with a K-means clustering algorithm to identify flood-prone areas. Microservices include fleet management, cloud computing, and data security.…”