A PROBABILISTIC INFORMATION INTERACTION MODEL FOR SMART CITY NETWORKS BASED ON TRAFFIC FLOW ANALYSIS

Abstract

The rapid growth of Internet of Things (IoT) technologies has significantly increased the amount of heterogeneous traffic generated in smart city environments. Traditional communication models cannot efficiently process massive real-time data streams because they do not adequately consider stochastic traffic behavior, communication delays, and distributed computing resources. This paper proposes a probabilistic information interaction model for Smart City networks based on traffic flow analysis. The proposed model integrates Software-Defined Networking (SDN), Fog computing, and Cloud computing into a unified communication architecture capable of supporting heterogeneous IoT applications. A probabilistic analytical model is developed to estimate temporal characteristics of information exchange by considering packet arrival intensity, service processes, and resource utilization. Furthermore, a simulation model is implemented to evaluate the proposed approach under different traffic loads. Experimental results demonstrate that the proposed model improves information delivery efficiency, reduces end-to-end delay, and provides better Quality of Service (QoS) compared with conventional network architectures. The proposed solution can be effectively applied to intelligent transportation systems, environmental monitoring, healthcare services, and other Smart City applications.