## 2 Related Work  
The literature provides a large body of work on the topic of data quality and validation. We first have a look at quality issues to work out how the use of the term relates to our view. Afterwards, we address the detection of inconsistent data in (I)IoT scenarios. We present semantic methods, complex event processing (CEP) systems, automatons, and machine learning methods. Furthermore, we investigate to what extent green computing has already found acceptance in the community. Additionally, our literature study includes related work with a focus on energy efficiency. The work of [PERSON] et al. [PERSON] et al. (2018) presents an overview of data quality and validation in big data environments. The authors point out that insufficient quality can lead to high costs for companies. In the literature, data quality often refers to aspects such as completeness, correctness, timeliness, accuracy, and availability (cf. [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018)). The criteria _completeness_ and _correctness_ are similar to those in our work. Thus, we also try to detect incomplete data sets and aim to keep correct information in the data. With our categories 3 and 4, we also specifically address the fact that content correctness of data may depend on a single message or a set of messages. The arrival of the messages plays a minor role, especially in our manufacturing scenario, since we handle long delays via timers or detection of overtakes. Accuracy of the data is not a criterion, as we do not aim to create a trust score with our automaton but to detect and subsequently report inconsistencies. [PERSON] et al. [PERSON] et al. (2018) also address _contextual anomalies_, which can only be detected with the addition of the context. However, the focus of the work is on individual values and not on detection in complex documents. The work of [PERSON] et al. [13] refers to data validation in the timber industry. The authors use SHACL shapes to define constraints. The work describes their real example case in detail and gives descriptive examples. Nevertheless,the authors do not provide a concrete implementation of their approach. Furthermore, the proposal is not intended to process big data streams. [PERSON] et al. [14] introduce a practical data stream scenario from the medical sector. The authors explain data validation techniques to address IoT problems in healthcare, but mainly evaluate data throughput to identify challenges in the big data area. Concrete implementations and experiments are missing. In [3; 15; 16], further approaches are presented. In contrast to our work, the presented methods do either use fixed knowledge bases, work on static data instead of streams, or are not intended to be applied in real-world IoT environments. Furthermore, none of these validation approaches focus on green computing solutions. The work of [PERSON] et al. [17] and [PERSON] et al. [18] present automatons for data validation. The authors refer to production plants and introduce time constraints. However, the authors do not mention detections of overtakes. This detection helps us to reduce the energy and resource consumption of the automaton. In [19], the authors provide an overview of further anomaly detection approaches to detect outliers in time series. In general, the problem is very similar to our data validation. Time series, however, usually contain much simpler data sets. In our work, we present an approach that tests complex content issues. Another possible solution to our data validation problems is complex event processing (CEP) systems. In preparation for this work, a literature search of existing CEP approaches has been performed. The literature search revealed that with [PERSON] [20], [PERSON] [21], and ETALIS [22], a large selection of CEPs exists. These systems allow the definition of simple constraints to detect patterns on a data stream. To do this, each of our consistency checks would have to be transformed into a pattern.