Design and Implementation of Edge Computing for Real-Time Data Processing in Internet of Things Applications

This research presented the design and evaluation of an edge computing architecture for efficient real-time data processing in Internet of Things environments. The study addressed the limitations of traditional cloud-based IoT systems, particularly high latency, excessive bandwidth consumption, and limited responsiveness in time-critical applications. By introducing an intermediate edge computing layer between IoT devices and cloud infrastructure, the proposed system enables data preprocessing and analysis closer to the data source. The experimental evaluation demonstrated that the edge-enabled architecture significantly improves system performance. The results showed a substantial reduction in latency and network bandwidth usage compared with conventional cloud-based architectures. The edge layer performs essential preprocessing tasks such as data filtering, aggregation, and local analytics, allowing cloud servers to focus on complex analytical operations and long-term data storage. This distributed processing approach enhances system scalability and enables efficient handling of large volumes of sensor-generated data. Furthermore, the proposed architecture supports a wide range of real-world applications including smart city management, healthcare monitoring systems, industrial automation, and smart agriculture solutions. These applications benefit from faster response times and improved decision-making capabilities enabled by edge computing. Future research can focus on integrating artificial intelligence and machine learning techniques within edge nodes to enable intelligent data processing and predictive analytics. Additionally, further studies may explore energy-efficient edge devices and enhanced security mechanisms to support large-scale IoT deployments.

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