Evaluating the Effectiveness of Edge Computing in Smart City Data Processing

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) devices and sensors in urban environments has heightened the demand for efficient data processing frameworks within smart cities, with edge computing emerging as a promising solution to address latency, bandwidth, and privacy concerns. This study aims to evaluate the effectiveness of edge computing architectures in enhancing data processing performance, reliability, and scalability in smart city environments. Specific objectives include assessing the impact of edge computing on data transmission latency, resource utilization, and system resilience; identifying key factors influencing the effectiveness of edge-based data processing; and providing actionable insights for urban planners and technology implementers to optimize smart city infrastructure. Employing a mixed-methods research design, this study combines quantitative experimental analysis with qualitative stakeholder interviews to provide a comprehensive evaluation. The target population comprises 50 smart city projects across metropolitan regions that have integrated edge computing solutions, with a stratified random sampling technique selecting a representative sample of 15 projects to ensure diversity in geographic location, urban scale, and technological maturity. Data collection instruments include system performance logs, network bandwidth measurements, and structured interview questionnaires developed based on established frameworks such as the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology (UTAUT). To ensure validity and reliability, quantitative instruments were pre-tested with a pilot study involving five projects, and triangulation techniques were employed to corroborate findings from different data sources. Analytical methods involve descriptive statistics to summarize system performance metrics, inferential statistical techniques such as multiple regression analysis to determine determinants of effective data processing, and ANOVA to compare performance variations across different project types. The qualitative interview data will be analyzed thematically to understand contextual factors affecting implementation and stakeholder perceptions. The study also applies the Resource-Based View (RBV) and Diffusion of Innovations theory to interpret how organizational capabilities and technological attributes influence the adoption and success of edge computing in urban environments. Expected findings include significant reductions in data transmission latency (anticipated average decrease of 35%), improved resource utilization efficiency (projected 25% reduction in network bandwidth consumption), and enhanced system resilience against network failures. The analysis is expected to reveal that factors such as infrastructure maturity, interoperability standards, and stakeholder engagement critically influence the deployment success of edge computing in smart city projects. The study also aims to identify disparities in performance outcomes based on city size, technological maturity, and socio-economic contexts. This research contributes to the growing body of knowledge by providing empirical evidence of the tangible benefits and challenges associated with edge computing in large-scale urban data ecosystems, thereby filling existing gaps related to contextualized performance evaluation and implementation barriers. It advances theoretical understanding by linking technology acceptance models with infrastructural and organizational factors within smart city environments. The main conclusion underscores that while edge computing significantly enhances data processing efficiency in smart cities, its success depends on strategic planning, standards interoperability, and stakeholder collaboration. The recommendations recommend adopting integrated frameworks for edge infrastructure deployment, emphasizing capacity building, and fostering public-private partnerships to accelerate adoption. Additionally, the study proposes avenues for further research, including longitudinal assessments of edge computing’s impact on urban service delivery and explorations of emerging technologies such as artificial intelligence and 5G to further optimize smart city data ecosystems.