Analysis of Smart City Sustainability Based on Big Data Integrated Edge Computing Modeling with Neural Network

The fact that urban areas are preferred places to live is evidence of a sophisticated system in which authorities can effectively satisfy citizens' needs and address economic, social, and environmental sustainability issues while facing several obstacles. The smart city idea improves sustainable development, human flourishing, and economic expansion. The smart city concept can potentially enhance a wide range of municipal operations, including transportation, energy, and healthcare, education, and resource management. Many network quality, stability, and security issues have arisen from developing smart cities and IoT applications. The dispersed nature of smart cities and IoT technology has led to problems with service reliability, longevity, and safety. This paper proposes Big Data Integrated Edge Computing Modeling (BDIECM) to improve data management interfaces' security and sustainability elements in various smart city applications to eliminate hazards. Edge computing allows for distributed processing and communication, useful in various contexts, including intelligent transportation systems, health and social services. Edge computing's ability to reduce latency is particularly useful for mission-critical applications. The system coordinates between dispersed edge servers and security mediator nodes for smart city use cases. The simulation results validate the suggested approach can significantly enhance the security ratio of95.2%, sustainability ratio of94.1 %,reliability ratio of 91.4 %, scalability ratio of 93.5 %, latency ratio of 21.1 %, and data transmission ratio of 96.2 % compared to other methods.