  Continued ubiquity of communication infrastructure across Internet of Things
(IoT) ecosystems has seen persistent advances of dynamic, intelligent,
virtualised sensing and actuation. This has led to effective interaction across
the connected ecosystem of -things. Furthermore, this has enabled the creation
of smart environments that has created the need for the development of
different IoT protocols that support the relaying of information across
billions of electronic devices over the Internet. That notwithstanding, the
phenomenon of virtual sensors that are supported by IoT technologies like
Wireless Sensor Networks (WSNs), RFID, WIFI, Bluetooth, ZigBee, IEEE 802.15.4,
etc., emulates physical sensors, and enables more efficient resource management
through the dynamic allocation of virtual sensor resources. A distinctive
example of this has been the proposition of the Dynamic Intelligent Virtual
Sensors (DIVS). This DIVS concept is a novel proposition that allows sensing to
be done by the use of logical instances through the use of labeled data. This
allows for making accurate predictions during data fusion. However, a potential
security attack on DIVS may end up providing false labels during the User
Feedback Process (UFP), which may interfere with the accuracy of DIVS. This
paper investigates the threat landscape in DIVS when employed in IoT
ecosystems, in order to identify the extent to which the severity of these
threats may hinder accurate prediction of DIVS in IoT, based on labeled data.
