OppIN: Optimal Path Intervention for Emergency Response Leveraging IoT and Big Data Technologies

Abstract

In this paper, we introduce the Optimal Path Intervention System (OppIN), a solution designed to support multiple emergency services, including fire response, civil protection, and emergency medical assistance, to reach crisis locations as quickly as possible by harnessing Big Data technologies and IoT infrastructure. OppIN computes quasi-real-time optimal intervention paths using a multi-criteria approach, incorporating both static factors (such as road network geometry, road conditions, and service locations) and dynamic data (including crisis locations captured by IoT sensors and real-time traffic conditions monitored through surveillance cameras). Using the IoT infrastructure and local data for quasi-real-time updates, OppIN adapts effectively to dynamic changes in context, ensuring the use of up-to-date information alongside Big Data technologies and AI for real-time processing. Compared to existing solutions such as Google Maps, our system uses a broader set of data sources and criteria, such as weather conditions, distance, traffic dynamics, and road status, to provide a more comprehensive and tailored analysis for specialized service navigation. Additionally, OppIN offers superior scalability and performance, using a Big Data-driven system design to handle high data volumes and real-time processing demands effectively. Furthermore, our system uses AI programs to estimate different criteria and to aggregate these criteria for quasi-real-time paths calculation.