The Secure-Cloud CDM (SC-CDM), designed by us, generates the encrypted CDM with a random key and initial vector and signs it with the public key of the CDM consumer to securely deliver the data before they are uploaded to IPFS. The encrypted CDM is lightweight via IPFS, but is maintained in transactions on a distributed ledger. The proposed system employs an immutable ledger on a message channel basis. It can manipulate and modify the current state of CDM. Finally, we evaluate the fragmented CDM algorithm in terms of elapsed time depending on the number and size of fragments while writing and reading the CDM.
As the data environment changes, it is evolving to a centralized computing centered on the cloud. But evaluating the reliability of the sensor data at the data sensor collection stage has a high cost limitation due to data transmission. To this end, application to edge computing is being applied in various fields. Edge computing allows computing to be performed in the closest place to the data, so that a lot of data is not transmitted to the central cloud and processed directly where the data is created, resulting in low latency and bandwidth efficiency.
This experiment evaluates the reliability of the sensor at the edge node where the sensor data is collected. A lightweight deep learning based LSTM[7] learning method is applied. LSTM is used to predict the temperature value of time series sensor data. In this paper, functional engineering that does not require much time is required, and reliability analysis of time series data is performed by data normalization. Therefore, this work shows that the LSTM network is an effective method for detecting and finding anomalies with the hidden data trend.