However, it is interesting to see how far this can be extended.
The \gls{MTASK} language is a high-level \gls{DSL}, so it is obvious to introduce abstractions for edge computations.
For example, add \gls{TOP} support for machine learning on the edge device using TinyML \citep{sanchez-iborra_tinyml-enabled_2020}.
+\Citet{van_der_veen_mutable_2020} did preliminary work for embedding bounded datastructures such as arrays to the language.
+This could be continued and extended with support for sum types.
Another recent advance in \gls{IOT} programming is battery-less or even battery-free computing.
Instead of equipping the edge device with a battery, a capacitor is used in conjunction with some energy harvesting systems such as a solar panel.
De Roos briefly investigated these methods in their research internship.
A future directions could be to extend these findings and apply more \gls{EDSL} error message techniques on \gls{MTASK} as well.
+\subsection{Language features}
The serialisation and deserialisation of data types is automated both on the server and the \gls{MTASK} device using generic programming.
Using the structural information of the data type, the code responsible for the functionality is automatically generated.
Peripherals are not yet fully integrated in such a way.
repositories / phd-thesis.git / blobdiff