Active Learning for IoT Data Prioritization in Edge Nodes Over Wireless Networks

Chen-Khong Tham, Rajalaxmi Rajagopalan

Abstract: The Internet of Things (IoT) has emerged as a key networking infrastructure that connects a large number of sensors, thereby allowing the collection and processing of large amounts of sensor data efficiently. Edge computing involves the deployment of computing devices close to sensor locations in order to process sensor data and derive useful information quickly and efficiently near the source. This improves the quality of the collected data and reduces the strain on the underlying communication networks. The obtained data can be used for a number of inference tasks using machine learning. The majority of machine learning research efforts assume that the collected data is untainted by various effects in the data collection or transmission process. The data-driven models generated with this assumption and the inferences drawn are frequently unusable in practical scenarios. Predictive maintenance is the process of utilizing sensor data obtained from equipment to determine and predict the optimal time for maintenance of the equipment. Using predictive maintenance, the time and money spent on maintaining equipment can be greatly reduced. In this paper, we perform distributed modeling using edge computing to determine the Remaining Useful Lifetime (RUL) of machines used in a manufacturing plant. We analyze the effects of wireless network degradation on the accuracy of the distributed models and propose active learning algorithms to mitigate these effects, thereby improving the usefulness and robustness of predictive maintenance in realistic settings.

External IDs:dblp:conf/iecon/ThamR20