INTEGRATION OF IOT AND EDGE COMPUTING FOR LOW-LATENCY DATA ANALYTICS IN SMART CITIES AND IOT NETWORKS

Abstract

This quantitative, cross-sectional, case-based study investigates how the integration of Internet of Things (IoT) infrastructures with edge computing architectures enhances low-latency data analytics and improves the quality of smart city services. Although smart city ecosystems continue to expand globally, a persistent challenge lies in their reliance on cloud-centric analytics models, which often struggle to satisfy stringent latency, reliability, and responsiveness requirements associated with time-sensitive public services such as traffic control, emergency response, environmental monitoring, and utility management. The central problem addressed in this study is that traditional cloud-dependent analytics pipelines frequently introduce processing delays and network congestion, thereby constraining the ability of municipalities and smart service operators to deliver real-time, high-quality services. This study therefore aims to provide empirical evidence, grounded in real IoT edge deployment scenarios across cloud and enterprise environments, regarding how integration quality and underlying infrastructure conditions shape latency outcomes and perceived service performance. Data were collected using a structured questionnaire administered to professionals directly involved in smart city, IoT systems design, and network infrastructure projects. Out of 250 distributed surveys, 200 valid responses were obtained, yielding an 80 percent usable response rate suitable for inferential analyses. Key constructs—including IoT Edge Integration Quality, Network Infrastructure Quality, System Reliability, Low Latency Analytics Performance, Smart City Service Quality, Adoption or Optimization Intention, and Perceived Integration Challenges—were measured using multi-item Likert scales. Analytical procedures included descriptive statistics, reliability and validity testing, Pearson correlations, and multiple regression modeling to examine predictive relationships among the variables. The regression models accounted for 56 percent of the variance in low-latency analytics performance and 61 percent of the variance in smart city service quality. IoT edge integration quality (β = 0.39), system reliability (β = 0.26), and network infrastructure quality (β = 0.17) emerged as significant predictors of low-latency analytics, underscoring the combined importance of architectural coherence, dependable system behavior, and communication efficiency. Furthermore, low-latency analytics performance demonstrated a strong positive effect on smart city service quality (β = 0.37), highlighting latency reduction as a key mechanism for improving user experience, operational responsiveness, and service effectiveness.