Functional Qos Metric For Lorawan Applications In Challenging Industrial Environment

Abstract

Industry 4.0 applications rely upon timely and accurate data about plant and process within a production site. Whilst modern facilities tend to have this capability as a matter of course, older equipment may lack network connectivity. A lack of data-gathering capability represents a significant barrier-to-entry when undertaking any data-driven investigation or improvement programs. Wireless sensor networks (WSNs) can be used as a flexible and low-disruption technique to acquire data at the point of interest, however the data stream is often lossy when deployed in harsh conditions without costly adaptations to the environment.This paper introduces the F-QoS metric which is able to classify the quality of the data stream from a WSN (using only packet reception timestamps), at user-defined sampling rates with a constraint placed upon the maximum amount of missing data. The resulting classifications can be used in an offline fashion to select periods of high-quality data for modelling, or, in an online manner to assess the realtime performance of a WSN.The F-QoS metric is applied to a LoRaWAN network in a large commercial bakery with a low-disruption installation-the network links are strained by large metal obstructions and the endpoints are installed inside metal cabinets. Each node transmits on a 10s cycle, and the analysis shows that >70% of the data is suitable for sampling at a 30s rate. The results indicate that LoRaWAN is capable of data acquisition in an unadapted and challenging environment, with the recommendation that the raw sample rate should be triple the desired final sample rate.