## 4 Conclusions  
This paper synthesizes previous DIVS-related results and incorporates them as IDIVS by focusing on mechanisms of machine learning and, in particular, interactive learning. The information properties and capabilities of the IDIVS have been further validated by identifying the requirements of learning mechanisms, including interactive learning and multi-layer learning. In general, the extra requirements of the IDIVS for achieving data fusion based on interactive machine learning are few. However, some types of information, such as property types sensed by sensors or outputted by the IDIVS and information gained, may be highly relevant to achieving transfer learning. Further, the capability to make a self-assessment of the output sensor value may be relevant for some active learning strategies. Moreover, the concept poses some new challenges related to information security. The requirements arising from the consideration of information security aspects have been highlighted, emphasizing the importance of taking user feedback into account in the context of information security. Obviously, tampering with user-supplied labels has a negative impact on the IDIVS output. Thus, we suggest that the user feedback channel be actively monitored to better safeguard the accuracy of the IDIVS. The IDIVS concept seems particularly suitable for interactive learning due to its capability to continuously provide output, which may create engagement for users to provide feedback (most straightforwardly by providing correct labels) to the IDIVS in order to improve its accuracy through machine learning, which is particularly useful in dynamic environments.  
Some future directions of research concern the IDIVS concept and the different learning mechanisms and challenges connected to it. One avenue of future research is to make use of other types of feedback for learning than labelled instances, e.g., the relative correctness of the output and implicit feedback from user behaviour, such as turning lighting on or off in the environment. Another avenue concerns the interaction modes connected to user feedback, e.g., using different physical artifacts or other methods of providing user feedback, to increase user engagement and ultimately system accuracy. In addition, exploring identification mechanisms for detecting erroneous user feedback, e.g., due to different interpretations of states, mistakes, or even non-benevolent users, and making use of that information in the learning process is another important topic to research. We believe that approaches such as those based on anomaly detection or voting approaches need adaptations to the IDIVS context.  
Other avenues concern the rapid set-up of IDIVS in practice, for instance, regarding the input needed from the installer to quickly achieve high accuracy in the IDIVS. Furthermore, how to efficiently use staged learning/machine teaching with different types of implicit or explicit user input is relevant to address. Finally, one may further consider the challenge of making use of feedback in a multilayer setting of IDIVSs, such that the learning can potentially propagate to other IDIVSs than the one directly receiving a label connected to its output.  
Conceptualization, All; methodology, All; software, J.H., A.T., R.-C.M. and J.B.; validation, All; formal analysis, All; data curation, A.T., J.H., R.-C.M. and J.B.; writing--original draft preparation, All; writing--review and editing, All; project administration, J.A.P.; funding acquisition, P.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by the Knowledge Foundation, grant number 20140035, Internet of Things and People Research Profile.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.