## 1 Introduction  
The integration of Internet of Things (IoT) technologies into smart city infrastructures has introduced new levels of automation, data-driven decision-making, and connectivity. However, this progress brings significant challenges related to data security, privacy, and system reliability--especially in cloud-based environments. In the case of UBT Smart City, a cloud-hosted IoT platform has been developed to collect, analyze, and store data from various environmental sensors using Microsoft Azure services. While initial implementations often focus on functional requirements and system scalability, security configurations are frequently addressed at a later stage [1]. As a result, the early architecture of the UBT IoT Cloud permitted unrestricted data flows between services, lacked encryption in transit, and was exposed to potential cyber threats due to public accessibility of critical components. Tomitigate these risks, a set of advanced security measures was implemented, including the use of Azure Virtual Networks, Network Security Groups (NSGs), routing tables, public IP restrictions, DDoS protection, and VPN-based access control. Although these changes significantly improved the system's security posture, they introduced potential overhead in communication latency and increased operational complexity. This paper aims to quantify the impact of these security mechanisms on the overall performance of the system by using a mathematical and statistical approach. We model system behavior through queueing theory, compare pre- and post-implementation delays, and evaluate cost-performance trade-offs using collected data. The ultimate goal is to provide a framework that helps smart city platforms optimize security settings while maintaining acceptable performance levels.  
Implementing advanced security mechanisms in the IoT Cloud architecture causes a statistically significant increase in data processing delay.  
The introduced security measures improve access control and system integrity without causing unsustainable cost escalation.  
Background and Related Work  
The evolution of smart cities has been largely driven by the adoption of IoT technologies, which facilitate real-time data collection, automated control, and intelligent decision-making across diverse domains such as traffic management, environmental monitoring, energy efficiency, and public safety [2; 3]. These systems rely heavily on distributed sensor networks and cloud-based platforms to store, process, and analyze vast volumes of data. While this architecture provides scalability and flexibility, it also introduces new attack surfaces and vulnerabilities that can be exploited if security is not adequately addressed [4].  
In a typical IoT Cloud deployment, data flows from edge devices (sensors or actuators) to centralized cloud services for further processing [5]. The Azure IoT architecture, for example, consists of services such as Azure IoT Hub for device communication, Stream Analytics for data processing, and Azure Structured Query Language Databases for storage [6; 7]. Without proper security measures, each component becomes a potential vector for unauthorized access, data breaches, or denial-of-service attacks.