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

Thesis Abstract

The burgeoning adoption of edge computing paradigms has transformed the landscape of real-time data processing by decentralizing computational resources closer to data sources, thereby promising potential improvements in latency reduction, bandwidth optimization, and system resilience. Despite the theoretical advantages, empirical evidence quantifying the actual impact of edge computing on data processing efficiency remains limited, necessitating a systematic evaluation to inform practitioners and researchers about its practical benefits and limitations. This study aims to assess the impact of edge computing deployment on real-time data processing efficiency, with specific objectives to measure processing latency, throughput, and energy consumption; compare performance metrics between edge-based and centralized cloud processing architectures; and identify factors influencing efficiency gains in diverse operational contexts. The research adopts a mixed-methods approach grounded in a quantitative experimental design complemented by qualitative insights from key stakeholders. The population comprises 25 industrial facilities across the manufacturing, healthcare, and transportation sectors that have integrated edge computing solutions within their operational infrastructure. A purposive sampling technique was used to select a sample of 10 facilities exhibiting varied scales of implementation. Data collection employed structured performance monitoring instruments embedded within the edge and cloud systems, capturing metrics over a four-month period, supplemented by semi-structured interviews with IT administrators and system engineers to elucidate contextual factors affecting system performance. Data analysis utilized multiple regression analysis to determine the relationship between system architecture (edge versus cloud) and processing efficiency indicators, and ANOVA tests to examine differences across industries and operational scales. Thematic analysis of qualitative interview data was conducted to contextualize quantitative findings and identify stakeholder perceptions, system challenges, and operational barriers. The study also applied the Technology Acceptance Model and the Diffusion of Innovations Theory to interpret adoption patterns and resistance levels. It is anticipated that the findings will reveal that edge computing significantly reduces processing latency and enhances throughput compared to traditional cloud-based systems, particularly in latency-sensitive applications such as industrial control and emergency response systems. Additionally, the study is expected to uncover that energy consumption varies depending on hardware specifications and workload intensity, with edge devices demonstrating more energy-efficient performance during peak operational periods. Factors such as network stability, data volume, and device interoperability are projected to influence the magnitude of efficiency gains, with contextual analyses highlighting operational and infrastructural nuances affecting deployment success. The contribution of this research lies in providing empirical benchmarks for evaluating edge computing’s performance benefits, thereby advancing the understanding of their practical implementation and operational impacts. It offers a comprehensive framework for organizations contemplating edge integration and informs future technological development, policy formulation, and strategic planning aimed at optimizing real-time data processing in heterogeneous environments. The study concludes that edge computing offers tangible improvements in processing efficiency; however, its benefits are moderated by infrastructural capacity and operational complexities. The recommendations include adopting standardized interoperability protocols, investing in resilient network infrastructure, and conducting sector-specific pilot evaluations before large-scale deployment. Future research should explore long-term cost-benefit analyses, scalability considerations, and the implications of emerging technologies such as 5G and artificial intelligence on edge-based data processing systems, thus contributing to the evolving body of knowledge at the intersection of distributed computing and real-time data management.