LAUSR

Storing data from Industrial-Internet-of-Things (IIoT) sensors in blockchain (BC) for monitoring the applications leads to management issues like bloating. The crux of this work is generating traces (the part of industrial data) using an ARIMA model and storing only the metadata over the network, resulting in reduced delay and managed data. We determine the size of the traces for storing on the store and generate (S&G) blocks (blocks that store traces along with their metadata) by considering principal parameters, such as training time, block size, and error. In general, S&G consists of three phases: 1) categorizing the data into groups based on their sampling rates, 2) storing the trace of data and metadata into the blocks, and 3) retrieving the entire data. We demonstrate the feasibility of S&G with errors and regret in the range of 0.07–0.10 and 0.20–0.25, respectively, using the appropriate ARIMA model.