Advancements in Coded Computation: Integrating Encoding Matrices with Data Shuffling for Enhanced Data Transmission Efficiency
Shijie Yuan · ITM Web of Conferences · 2025
In the interconnected age of big data, cloud computing, and the Internet of Things, the demand for robust data processing and transmission systems is critical. This study delves into the fundamental principles, technological advantages, and applications of coded computation, emphasizing the integration of encoding matrices and data shuffling techniques. Encoding matrices enhance data reliability, fault tolerance, and security, reducing transmission and storage costs. Data shuffling techniques, by reordering data, decrease communication overhead and computational burden, thereby optimizing the coding computation process. This paper analyzes various data shuffling methods, their integration with encoding matrices, and their impact on computational efficiency and data transmission. The application of these technologies promises substantial improvements in the efficiency of data systems, offering vital advancements for modern computing environments. By refining the design of encoding matrices and data shuffling strategies, the potential to elevate the performance of coded computations is explored, with implications for the progressive development of information technology.