CLUSTER-BASED GRID COMPUTING ON WIRELESS NETWORK DATA TRANSMISSION WITH ROUTING ANALYSIS PROTOCOL AND DEEP LEARNING

Grid computing based on clusters has emerged as a promising strategy for improving the efficacy of wireless network data transmission. This study examines the incorporation of cluster-based grid computing, routing analysis protocols, and deep learning techniques to optimize data transmission in wireless networks. The proposed method utilizes clusters to distribute computing duties and enhance resource utilization, resulting in efficient data transmission. To further improve the routing process, a novel routing analysis protocol is introduced, which dynamically adapts to network conditions and chooses the most optimal routes. In addition, deep learning algorithms are used to analyze network data patterns, allowing for intelligent data routing and resource allocation decisions. Experiment results exhibit the efficacy of the proposed method, revealing substantial enhancements in network performance metrics such as throughput, latency, and energy consumption. This research contributes to the development of cluster-based grid computing and offers valuable insights for the design of efficient wireless network data transmission systems.