Evaluating the Impact of Edge Computing on Latency in Smart City Networks

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) devices and sensor networks in smart city environments has elevated the critical importance of low-latency communication to ensure efficient service delivery, safety, and real-time decision-making. Despite significant advancements in centralized cloud computing, latency issues remain a considerable challenge in dynamic, data-intensive urban scenarios. This study aims to evaluate the impact of edge computing deployment on latency reduction within smart city networks, providing empirical evidence to guide infrastructure development and policy formulation. Specific objectives include quantifying latency improvements attributable to edge computing, identifying optimal edge node placements, and analyzing the relationship between network load and latency performance. The research adopts a mixed-methods approach, integrating quantitative measurement and qualitative insights to comprehensively evaluate the impact of edge computing. The primary population consists of 150 sensor nodes and associated data processing points distributed across a metropolitan area characterized by high-density IoT deployment. A stratified random sampling technique selects 60 representative nodes for detailed latency measurement, while semi-structured interviews are conducted with 20 network administrators and urban planners to contextualize quantitative findings. Data collection instruments comprise high-precision network analyzers for capturing real-time latency metrics, along with structured interview guides. Quantitative data analysis employs descriptive statistics and inferential techniques such as multiple regression analysis to assess the influence of edge computing deployment variables on latency reduction. ANOVA tests evaluate differences in latency performance across various network segments and configurations, while spatial analysis tools identify correlations between edge node placement and network performance improvements. Theoretical underpinning is grounded in the Distributed Cognition theory, which explicates how decentralized processing influences system efficiency, and the Network Optimization theory, providing a framework for understanding latency minimization strategies. The study also applies the Technology Acceptance Model to explore stakeholder perspectives on edge computing adoption. Expected findings indicate that strategic deployment of edge nodes significantly reduces latency, with average latency improvements of approximately 35% compared to traditional cloud-dependent models. Edge placement proximal to high-traffic sensor hubs further enhances performance, particularly under elevated network loads. Qualitative insights reveal organizational and infrastructural factors influencing successful edge integration, including stakeholder readiness, technical expertise, and existing network capacity. These findings contribute to the growing body of knowledge by empirically validating the efficacy of edge computing in urban contexts and delineating best practices for network design. The study’s primary contribution lies in providing a rigorous empirical framework for evaluating edge computing’s role in latency mitigation within smart city networks, integrating both technical and organizational perspectives. It demonstrates that targeted edge deployment can markedly improve network responsiveness, thereby enhancing urban services such as traffic management, emergency response, and environmental monitoring. In conclusion, the research advocates for strategic, data-driven edge infrastructure investments aligned with urban growth trajectories and technological capacity. Recommendations include adopting a decentralized architecture model prioritizing sensor traffic hotspots, establishing standardized protocols for edge node placement, and fostering stakeholder collaboration to overcome infrastructural and knowledge barriers. Finally, the study suggests avenues for future research, such as longitudinal assessments of edge deployment impacts and exploration of emerging technologies like 5G and artificial intelligence in conjunction with edge computing to further optimize smart city networks.