-\section{Motivation}
-\Gls{TOP} and \gls{iTasks} have been designed to offer a high abstraction level
-through a \gls{DSL} that describes workflows as \glspl{Task}. \gls{iTasks} has
-been shown to be useful in fields such as incident
-management~\cite{lijnse_top_2013}. However, there still lacks support for small
-devices to be added in the workflow. In principle such adapters can be written
-as \glspl{SDS}\footnote{Similar as to resources such as time are available in
-the current system} but this requires a very specific adapter to be written for
-every device and functionality. Oortgiese et al.\ lifted \gls{iTasks} from a
-single server model to a distributed server architecture~\todo{Add cite} that
-is also runnable on smaller devices like \acrshort{ARM}. However, this is
+\section{Introduction}
+\Gls{IoT} technology is emerging very quickly and offers myriads of solutions
+and transforms the way we interact with technology. Initially the term was
+coined to describe \gls{RFID} devices and the communication between them.
+However, currently the term \gls{IoT} encompasses all small devices that
+communicate with each other and the world often containing sensors, \gls{GPS}
+and actuators\cite{da_xu_internet_2014}. With these new technologies
+information can be tracked very accurately using very little power and
+bandwidth. Moreover, \gls{IoT} technology is coming into people's homes,
+clothes and in healthcare\cite{riazul_islam_internet_2015}. For example, for a
+couple of tens of euros a consumer ready fitness tracker watch can be bought
+that tracks heartbeat and respiration levels.
+
+The \gls{TOP} paradigm and the according \gls{iTasks} implementation offer a
+high abstraction level for real life workflow tasks%
+\cite{plasmeijer_itasks:_2007}. These workflow tasks can be described through
+an \gls{EDSL} and modeled as \glspl{Task} From the specification the system
+will then generate a multi-user web service. This web service is accessed
+through a browser and used to complete these \glspl{Task}. Familiar workflow
+patterns like sequence, parallel and conditional tasks can be modelled using
+combinators.
+
+\gls{iTasks} has been shown to be useful in many fields of operation such as
+incident management~\cite{lijnse_top_2013}. Interfaces are automatically
+generated for the types of data which makes rapid development possible.
+\Glspl{Task} in the \gls{iTasks} system are modelled after real life workflow
+tasks but the modelling is applied on a very high level. Therefore it is
+difficult to connect \gls{iTasks} tasks to the real world tasks and let them
+interact. A lot of the actual tasks could be \emph{performed} by small
+\gls{IoT} devices. Nevertheless, adding such devices to the current system is
+difficult to say the least as it was not designed to cope with these devices.
+
+In the current system such adapters, in principle, can be written as
+\glspl{SDS}\footnote{Similar as to resources such as time are available in
+the current \gls{iTasks} implementation} but this requires a very specific
+adapter to be written for every device and functionality. However, this forces
+a fixed logic in the device that is set at compile time. A lot of the small
+\gls{IoT} devices have limited processing power but can still contain decision
+making. Oortgiese et al.\ lifted \gls{iTasks} from a single server model to a
+distributed server architecture that is also runnable on smaller devices like
+\acrshort{ARM} devices\cite{oortgiese_distributed_2017}. However, this is