Evaluating the Impact of Edge Computing on Real-Time Data Processing Efficiency

Thesis Abstract

The rapid proliferation of Internet of Things (IoT) devices and the increasing demand for instantaneous data processing have underscored the critical role of edge computing in enhancing the efficiency of real-time data management. Despite the widespread adoption of edge computing architectures, empirical evidence evaluating their specific impact on data processing speed, latency reduction, and overall system performance remains limited. This study aims to comprehensively evaluate the impact of edge computing on real-time data processing efficiency within industrial IoT environments. The primary objectives include quantifying processing delays before and after edge deployment, analyzing resource utilization, and identifying operational factors influencing performance gains. A mixed-methods research design was employed, integrating quantitative measurements with qualitative insights. The quantitative component involved a quasi-experimental setup with an industrial manufacturing plant as the population, comprising 120 IoT-enabled devices categorized into two groups those processed centrally and those processed at the edge. A stratified random sampling technique selected 60 devices from each group, ensuring representativeness across device types and operational zones. Data collection instruments included network performance monitoring tools, latency measurement software, and system logs spanning a six-month operational period. Additionally, semi-structured interviews with 15 system engineers supplemented quantitative data, providing contextual understanding of implementation challenges and operational behaviors. Data analysis incorporated descriptive statistics to summarize processing times and resource utilization, inferential statistics—specifically paired t-tests and multiple regression analysis—to evaluate differences and identify predictors of efficiency, and thematic analysis for qualitative insights. The study anticipates discovering that edge computing significantly reduces processing latency by an average of 35%, lowers network bandwidth consumption by 22%, and enhances resource utilization efficiency, especially during peak operational periods. These findings imply that deploying edge solutions can improve real-time responsiveness, critical for process optimization and fault detection in industrial settings. The study’s contribution to knowledge lies in providing rigorous empirical evidence quantifying the operational benefits of edge computing, thereby filling existing gaps related to performance metrics in industrial IoT contexts. It also offers a framework for organizations to assess the suitability of edge deployment based on specific operational parameters and technological configurations. The main conclusion emphasizes that strategic integration of edge computing architectures markedly enhances data processing efficiency, providing tangible performance improvements that support predictive maintenance and real-time decision-making. Based on these outcomes, the study recommends that industrial organizations prioritize the adoption of edge computing solutions, tailor deployment strategies based on operational workload and device heterogeneity, and invest in robust system monitoring tools to maximize benefits. Future research should explore long-term deployment effects, scalability challenges, and the economic implications of broader edge computing integration across diverse industrial sectors.