论文标题
决策触发了工业互联网中的数据传输和收集
Decision Triggered Data Transmission and Collection in Industrial Internet of Things
论文作者
论文摘要
我们建议在工业互联网（IIOT）中触发数据传输和收集协议（DTDTC）协议，以进行状态监测和异常检测。在IIOT中，传感器读取的集合，处理，编码和传输通常不是用于重建原始数据，而是用于融合中心的决策。通过将决策过程移至本地设备，可以大大减少数据传输量，尤其是当正常信号在整个生命周期中占主导地位时，融合中心仅感兴趣收集异常数据时。提出的概念结合了压缩感测，机器学习，数据传输和联合决策。仅当检测到具有负面决策的异常信号时，传感器读数才被编码并传输到融合中心。最终设备的所有异常信号都聚集在Fusion Center，以共同决定，并将反馈消息转发给当地执行者。这种方法的优点在于它可以显着减少通过无线链接传输的数据量。此外，引入压缩传感可以大大降低数据的维度。提供了一个示例性的情况，即柴油发动机状况监测，以验证与常规方案相比，拟议方案的有效性和效率。
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.
