###### Abstract  
This paper concerns the novel concept of an Interactive Dynamic Intelligent Virtual Sensor (IDIVS), which extends virtual/soft sensors towards making use of user input through interactive learning (IML) and transfer learning. In research, many studies can be found on using machine learning in this domain, but not much on using IML. This paper contributes by highlighting how this can be done and the associated positive potential effects and challenges. An IDIVS provides a sensor-like output and achieves the output through the data fusion of sensor values or from the output values of other IDIVSs. We focus on settings where people are present in different roles: from basic service users in the environment being sensed to interactive service users supporting the learning of the IDIVS, as well as configurators of the IDIVS and explicit IDIVS teachers. The IDIVS aims at managing situations where sensors may disappear and reappear and be of heterogeneous types. We refer to and recap the major findings from related experiments and validation in complementing work. Further, we point at several application areas: smart building, smart mobility, smart learning, and smart health. The information properties and capabilities needed in the IDIVS, with extensions towards information security, are introduced and discussed. virtual sensors; soft sensors; sensor data fusion; online learning; interactive machine learning; transfer learning; information security +
Footnote †: journal: _achemology_
information security, because new types of attacks on the system can be launched, e.g., by having people manipulate the classification scheme to fit their own agenda. For the IDIVS to produce as much accuracy as possible and as quickly as possible after being installed, we also explore transfer learning and user interaction interfaces, which may be particularly relevant in cold start situations. By using these different learning mechanisms, an IDIVS is also able to handle situations where the set of sensors streaming data is not fixed over time, but dynamic. Sensors may malfunction or lose network connectivity and stop providing sensor data. Sensor data may come from portable devices, such as smartphones, which only appear and provide sensor data temporarily. Further, dedicated stationary devices with sensors may be installed in or removed from the environment permanently. The environment may also pose challenges, e.g., sensors and physical objects, such as chairs, may be moved. Hence, it is important that the IDIVS is adaptable. An important mechanism for the IDIVS to cope with the dynamicity of the sensors and the environment is again the online machine learning capability, primarily based on the fact that there are humans present that can provide feedback. The vision of the IDIVS is that it can constitute a piece of software that can be re-used as a building block where a service needs information on the current state or context. The output of the IDIVS may be used by different applications/services. The concept creates opportunities to better meet the local/contextual needs of providing services in different settings (buildings, transport, health applications, etc.). Hence, we argue it constitutes the missing link between streamed sensor data from multiple systems and the services/applications layers to enable new or improved value-added services. The purpose of this paper is to further expand the earlier suggested DIVS [1,2] concept towards being more refined concerning aspects connected to interactive machine learning and transfer learning, and to highlight the information security aspects that come into play due to the learning. The paper will also point to some recent related work that validates a number of properties of the suggested concept. Hence, the paper contributes by clarifying how interactive machine learning has the potential to improve state detection in a number of application domains by extending the concept of virtual sensors. Further, it highlights the challenges that come with this extension.