1. Submitted to the IEEE Transactions on Industrial Informatics. (June 26, 2020)
2. Status: Awaiting Co-EIC Assignment (June 27, 2020)
3. Status: Assigning Reviewers (July 03, 2020)
4. Status: Assign additional Reviewers (July 13, 2020)
5. Status: Awaiting Review Scores (July 21, 2020)
6. Status: Awaiting AE Recommendation (August 10, 2020)
This paper presents a communication of Manufacturing Message Specification (MMS) protocol over blockchain architecture for Industrial IoT machine-to-machine environment between server and several clients. A 1D Convolutional Neural Network (CNN) is used for event classification of the raw time-series sensor data which installed in the MMS server. It is intended to give clients information about the condition of the environment to prevent danger. The weight and the model structure of the training process are saved into a hierarchical data format (HDF5) and YAML format respectively, which later be used for the real-time testing process. The proposed 1D CNN architecture with standard scaler pre-processing overcomes the previous architecture framework in terms of accuracy up to 99.60\% for training, while 92.52\% for testing process. Also, the processing time is only required 312 us/step. In addition, a real-time event classification based on sensor data is constructed in the implementation. Meanwhile, real objects¡¯ values from the sensors are converted into MMS objects¡¯ values inside a virtual manufacturing device (VMD). Each object is declared with its own unique object name. Then MMS objects' values will be stored to the private blockchain network using a static difficulty number. In the private blockchain network, registered MMS clients can access every newly mined block and its MMS objects¡¯ values. The data is secured inside the private blockchain network as only registered participants can access it.