CTS-IIoT: Computation of Time Series Data During Index Based De-duplication of Industrial IoT (IIoT) Data in Cloud Environment

In recent times, the exponential boom of industrial data in cloud is witnessed due to dramatic outcome of digitization and smart environment within the industries. Globalization, easy-to-use and availability of data also plays a key role in driving data production in the cloud environment. However, many industries and organizations use several techniques to collect and process massive amount of data which is obtained through various data acquisition channels. Processing of such huge data with redundancy rate has an impact on time series analysis and cloud storage as well. Hence, an integrated technique to perform data de-duplication and time series analysis is required. Furthermore, optimal location to place the data also become an essential for efficient access of data in the cloud environment. To address the aforementioned issues, the proposed system presents CTS-IIoT: Computation of Time Series data during Index Based De-duplication of Industrial IoT (IIoT) data in Cloud Environment to compute time series data during de-duplication using Merkle Hash Tree (MHT). Finally, the proposed system concludes with the determination of optimal location with minimal transportation cost to reach the storage nodes in the cloud environment using Modified Distribution (MODI) method. The experimental results reveal that the proposed model is efficient since it facilitates less memory and less computation overhead. The proposed technique achieves space reduction by 43%, reduces the computation overhead by 32% and increases the efficacy of data retrieval by 18.5%.