Sensor Fusion Techniques for Comprehensive Environmental Monitoring in Smart Cities

Smart cities leverage advanced technologies to systematically monitor and assess urban environments. Multi-sensor integration, the process of combining data from diverse sensors, is critical for generating reliable and comprehensive environmental insights. This paper presents a detailed analysis of sensor fusion methodologies tailored for smart city applications, specifically focusing on the integration of air quality, noise levels, temperature, and humidity sensors. A weighted average fusion model and a Support Vector Machine (SVM) classification approach are developed and evaluated. The effectiveness of these methodologies is assessed through real-time data, error analysis, and performance comparisons, highlighting their potential for real-time environmental monitoring. In a similar context, the findings demonstrate improved accuracy and reliability of fused data, with recommendations for optimizing urban management strategies. Furthermore, the study underscores the importance of real-time processing capabilities and explores the implications of these technologies for city planners and policymakers. The paper concludes with insights into the advantages of smart city applications and proposes directions for future research in sensor-based urban management systems.