Efficient Data Storage Algorithm for IoT in Cloud-Distributed Environments

The Contemporary IoT implementations rely on cloud computing for data storage, encountering challenges such as suboptimal data processing performance and a diminished fault tolerance rate. In response, this research introduces an innovative approach employing Hadoop Distributed File System (HDFS) to enhance access to storage techniques in cloud computing environments. HDFS is strategically employed to refine the data access storage architecture within the IoT framework by considering various factors influencing the data access storage distribution. Hash values are harnessed to optimize the configuration of data access metadata storage locations, thereby refining the data access storage distribution strategy. The optimization process extends to the IoT topology, where considerations for data block distribution are meticulously addressed using an effective methodology. This holistic approach ensures that the IoT ecosystem is not only architecturally optimized but also that the data block allocation with the overall efficiency goals. The proposed methodology is validated through comprehensive simulations and real-world experiments that simulate diverse IoT scenarios. The results demonstrate a substantial enhancement in data storage efficiency, reduced latency, and improved overall system reliability when compared to conventional storage techniques. Additionally, the implementation incorporates robust security measures, including encryption, access control, and authentication protocols, aligning with contemporary standards for safeguarding sensitive IoT data.