Section: New Results

Introducing Data Quality to the Internet of Things

Participants : Jean-Marie Bonnin, Frédéric Weis [contact] .

The Internet of Things (IoT) connects various distributed heterogeneous devices. Such Things sense and actuate their physical environment. The IoT pervades more and more into industrial environments forming the so-called Industrial IoT (IIoT). Especially in industrial environments such as smart factories, the quality of data that IoT devices provide is highly relevant. However, current frameworks for managing the IoT and exchanging data do not provide data quality (DQ) metrics. Pervasive applications deployed in the factory need to know how data are "good" for use. However, the DQ requirements differ from a process to another. Actually, specifying/expressing DQ requirements is a subjective task, depending on the specific needs of each targeted application. As an example this could mean how accurate a location of an object that is provided by an IoT system differs from the actual physical position of the object. A Data Quality of 100% could mean that the value represents the actual position. A Data Quality of 0% could mean that the object is not at the reported position. In this example, the value 0% or 100% can be given by a specific software module that is able to filter raw data sent to the IoT system and to deliver the appropriate metric for Dev apps. Building ad hoc solutions for DQ management is perfectly acceptable. But the challenge of writing and deploying applications for the Internet of Things remains often understated. We believe that new approaches are needed, for thinking DQ management in the context of extremely dynamic systems that is the characteristic of the IoT.

In 2019, we introduced DQ to the IoT by (1) representing data quality parameters as metadata to each stored and exchanged IoT data item and (2) providing a toolbox that helps developers to assess the data quality of their processed data using the previously introduced data quality metadata. We followed an inductive approach. Therefore, we set up a pilot to gain first-hand experience with DQ, and to test our developed tools. Our pilot focuses on multi-source data inconsistency. Our setting consists of multiple industrial robots that cowork within a factory. The robots on the line follow a fixed path while the other two robots can freely move. For our implementation we use a data-centric IoT middleware, the Virtual State Layer (VSL). It provides many desired properties such as security and dynamic coupling of services at runtime. Most important it has a strong semantic model for representing data that allows adding new metadata for data quality easily. In our pilot the decrease of the DQ is caused by a low periodicity of location reports. We implemented a DQ service that infers the DQ being located in the service chain. The coordination service queries our DQ enriching service. The DQ enrichment service models the behavior of a robot and infers the resulting DQ depending on the time between the location report and the coordination service’s query. Our goal was not only to report the DQ to the consuming service but also to offer tools (microservices) to mitigate from bad DQ. To enable a mitigation from the decreasing DQ, we started the sensors at a random time. This results in the same precision decrease periodicity but in shifted reporting times. The shift enables increasing the DQ by using sensor fusion and data filtering.