Abstract

Efficient data management is a crucial topic in the dynamic realm of Smart Cities, as it plays a critical role in effectively using the potential of the Internet of Everything (IoE) technology inside urban environments. This study highlights the significant significance of efficient data management in Smart Cities, with particular emphasis on its contribution to informed decision-making and the optimization of resources. Nevertheless, the substantial increase in data in this context has resulted in security apprehensions since current approaches often need to tackle these difficulties comprehensively. This research presents the Security Model with Data Management Cloud-assisted Internet of Everything (SMDM-CIoE) Framework as a novel solution. This approach integrates cutting-edge technology capabilities to improve security measures and data management efficiency using Dynamic Regression Framework (DRF). The SMDM-CIoE method incorporates a robust system model integrating IoE and cloud-based data management, application deployment, and an Improved DRF helps to enhance security. This integration is crucial in addressing the complex and interrelated challenges of Smart Cities, ensuring a holistic approach to security and data optimization. In the context of rigorous simulations, utilizing SMDM-CIoE exhibited noteworthy outcomes, manifesting a significant decrease of 30% in security breaches compared to prevailing methodologies. Using an enhanced DRF has resulted in a notable 20% augmentation in data processing speed, improving the ability to make real-time decisions. This underscores the importance of SMDM-CIoE as a crucial instrument in influencing the future development of Smart Cities. Data protection is evident in the suggested approach's 6.29% Security Breach Rate. Efficiency is enhanced by 252.61 Mbps data processing. 68.60% Resource Utilization indicates balanced and effective system resource allocation, boosting performance and sustainability. Information transit is reliable with 92.60% Data Transmission Reliability. Scalability is 83.35%, indicating system adaptability and expansion prospects.