Enhancing Urban Resilience in Smart Cities Through Edge AI, Point of Interest Analysis, and Multi-Criteria Decision-Making

In today's urban environment, where extreme weather, epidemic outbreaks, and social security threats are frequent, the construction of smart cities must focus not only on efficiency and convenience but also on defensive resilience to handle crises effectively. This study proposes an innovative information service architecture that integrates edge artificial intelligence (Edge AI), point of interest (POI) analysis, and multi-criteria decision analysis (MCDA) to enhance early warning and solution recommendation processes for smart city incidents. The proposed system will identify critical POI maps in the city through POI risk analysis. Edge AI devices will be strategically placed at these key POIs to detect real-time abnormal events. The service system will evaluate various influencing factors using service-oriented computing and utilize the Analytical Hierarchy Process (AHP) combined with MCDA decision models to provide optimal decision support for candidate solutions based on multiple variables. The anticipated contribution of this research is to help smart cities shorten incident response times and optimize the quality of disaster management decisions. Additionally, using information technology for service-oriented computing will facilitate the establishment of a real-time, modular, and scalable urban defense system. This system will offer optimal strategies to equip smart cities better to face unexpected risks.