-such as time are available in the current \gls{iTasks} implementation}.
-However, this
-requires a very specific adapter to be written for every device and function.
-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 limited to fairly high performance devices that are equipped
-with high speed communication channels. Devices in \gls{IoT} often have only
-\gls{LTN} communication with low bandwidth and a very limited amount of
-processing power and are therefore not suitable to run an entire \gls{iTasks}
-core.
+such as time are available in the current \gls{iTasks} implementation.}.
+However, this requires a very specific adapter to be written for every device
+and function. This forces a fixed logic in the device that is set at compile
+time. Many small \gls{IoT} devices have limited processing power but are still
+powerfull enough for decision making. Recompiling the code for a small
+\gls{IoT} device is expensive and therefore it is difficult to use a device
+dynamically for multiple purposes. Oortgiese et al.\ lifted \gls{iTasks} from a
+single server model to a distributed server architecture that is also runnable
+on small devices such as those powered by
+\gls{ARM}~\cite{oortgiese_distributed_2017}. However, this is limited to
+fairly high performance devices that are equipped with high speed communication
+channels because it requires the device to run the entire \gls{iTasks} core.
+Devices in \gls{IoT} often have only Low Throughput Network communication with
+low bandwidth and a very limited amount of processing power and are therefore
+not suitable to run an entire \gls{iTasks} core.