  We propose a decision triggered data transmission and collection (DTDTC)
protocol for condition monitoring and anomaly detection in the industrial
Internet of things (IIoT). In the IIoT, the collection, processing, encoding,
and transmission of the sensor readings are usually not for the reconstruction
of the original data but for decision making at the fusion center. By moving
the decision making process to the local end devices, the amount of data
transmission can be significantly reduced, especially when normal signals with
positive decisions dominate in the whole life cycle and the fusion center is
only interested in collecting the abnormal data. The proposed concept combines
compressive sensing, machine learning, data transmission, and joint decision
making. The sensor readings are encoded and transmitted to the fusion center
only when abnormal signals with negative decisions are detected. All the
abnormal signals from the end devices are gathered at the fusion center for a
joint decision with feedback messages forwarded to the local actuators. The
advantage of such an approach lies in that it can significantly reduce the
volume of data to be transmitted through wireless links. Moreover, the
introduction of compressive sensing can further reduce the dimension of data
tremendously. An exemplary case, i.e., diesel engine condition monitoring, is
provided to validate the effectiveness and efficiency of the proposed scheme
compared to the conventional ones.
