Tasks can exchange information via \glspl{SDS} \citep{ParametricLenses}.
All tasks involved can atomically observe and change the value of a typed \gls{SDS}, allowing more flexible communication than with task combinators.
\Glspl{SDS} offer a general abstraction of data shared by different tasks, analogous to variables, persistent values, files, databases and peripherals like sensors. Combinators compose \glspl{SDS} into a larger \gls{SDS}, and
-parametric lenses define a specific view on an \gls{SDS}.
+parametric lenses define a specific view on \pgls{SDS}.
\subsection{The \texorpdfstring{\glsentrytext{ITASK}}{iTask} \texorpdfstring{\glsxtrlong{EDSL}}{eDSL}}%
\label{sec_t4t:itasks}
A typical \gls{IOT} system goes beyond a web application by incorporating a distributed set of sensor nodes each with a collection of sensors or actuators. That is, they add the perception and network layers in \cref{fig_t4t:iot_arch}. If the sensor nodes have the computational resources to support an \gls{ITASK} server, as a Raspberry Pi does, then \gls{ITASK} can also be used to implement these layers, and integrate them with the application and presentation layers tierlessly.
As an example of tierless \gls{IOT} programming in \gls{CLEAN}\slash\gls{ITASK} \cref{lst_t4t:itaskTempFull} shows a complete temperature sensing system with a server and a single sensor node (\gls{CRTS}), omitting only the module name and imports.
-It is similar to the SimpleTempSensor and TempHistory programs above, for example \cleaninline{devTask} repeatedly sleeps and records temperatures and times, and \cleaninline{mainTask} displays the temperatures on the web page in \cref{fig_t4t:cwtsweb}. There are some important differences, however. The \cleaninline{devTask} (\crefrange{lst_t4t:itaskTempFull:sensorfro}{lst_t4t:itaskTempFull:sensorto}) executes on the sensor node and records the temperatures in a standard timestamped (lens on) an \gls{SDS}: \cleaninline{dateTimeStampedShare} \cleaninline{latestTemp}.
+It is similar to the SimpleTempSensor and TempHistory programs above, for example \cleaninline{devTask} repeatedly sleeps and records temperatures and times, and \cleaninline{mainTask} displays the temperatures on the web page in \cref{fig_t4t:cwtsweb}. There are some important differences, however. The \cleaninline{devTask} (\crefrange{lst_t4t:itaskTempFull:sensorfro}{lst_t4t:itaskTempFull:sensorto}) executes on the sensor node and records the temperatures in a standard timestamped (lens on) \pgls{SDS}: \cleaninline{dateTimeStampedShare} \cleaninline{latestTemp}.
The \cleaninline{mainTask} (\cref{lst_t4t:itaskTempFull:main}) executes on the server: it starts \cleaninline{devTask} as an asynchronous task on the specified sensor node (\cref{lst_t4t:itaskTempFull:startdevtask}) and then generates a web page to display the latest temperature and time (\cref{lst_t4t:itaskTempFull:displaystart,lst_t4t:itaskTempFull:displayend}).
The \cleaninline{tempSDS} is very similar to the \cleaninline{measurementsSDS} from the previous listings.
The program uses the same shares \cleaninline{tempSDS} and~\cleaninline{latestTemp} as \gls{CRTS}, and for completeness we repeat those definitions.
The body of \cleaninline{devTask} is the \gls{MTASK} slice of the program (\crefrange{lst_t4t:mtasktemp:DHT}{lst_t4t:mtasktemp:setSds}).
With \cleaninline{DHT} we again create a temperature sensor object \cleaninline{dht}.
-The \gls{ITASK} \gls{SDS} \cleaninline{latestTemp} is first transformed to a \gls{SDS} that accepts only temperature values,
+The \gls{ITASK} \gls{SDS} \cleaninline{latestTemp} is first transformed to \pgls{SDS} that accepts only temperature values,
the \cleaninline{dateTimeStampedShare} adds the data via a lens.
The \cleaninline{mapRead} adjusts the read type.
This new \gls{SDS} of type \cleaninline{Real} is lifted to the \gls{MTASK} program with \cleaninline{liftsds}.
In \gls{PRS} the sensor node program is written in \gls{PYTHON}, a language far less focused on minimising memory usage than \gls{MICROPYTHON}. For example an object like a string is larger in \gls{PYTHON} than in \gls{MICROPYTHON} and consequently does not support all features such as \emph{f-strings}.
Furthermore, not all advanced \gls{PYTHON} feature regarding classes are available in \gls{MICROPYTHON}, i.e.\ only a subset of the \gls{PYTHON} specification is supported \citep{diffmicro}.
-In summary the sensor node code generated by both tierless languages, \gls{ITASK} and \gls{MTASK}, is sufficiently memory efficient for the target sensor node hardware. Indeed, the maximum residencies of the \gls{CLEAN} sensor node code is less than the corresponding hand-written (Micro)\gls{PYTHON} code. Of course in a tiered stack the hand-written code can be more easily optimised to minimise residency, and this could even entail using a memory efficienthat thet language like \gls{C}\slash\gls{CPP}. However, such optimisation requires additional developer effort, and a new language would introduce additional semantic friction.
+In summary the sensor node code generated by both tierless languages, \gls{ITASK} and \gls{MTASK}, is sufficiently memory efficient for the target sensor node hardware. Indeed, the maximum residencies of the \gls{CLEAN} sensor node code is less than the corresponding hand-written (Micro)\gls{PYTHON} code. Of course in a tiered stack the hand-written code can be more easily optimised to minimise residency, and this could even entail using a memory efficient language like \gls{C}\slash\gls{CPP}. However, such optimisation requires additional developer effort, and a new language would introduce additional semantic friction.
\paragraph{Power} Sensor nodes and sensors are designed to have low power demands, and this is particularly important if they are operating on batteries. The grey literature consensus is that with all sensors enabled a sensor node should typically have sub-\qty{1}{\watt} peak power draw.
The \gls{WEMOS} sensor nodes used in \gls{CWS} and \gls{PWS} have the low power consumption of a typical embedded device: with all sensors enabled, they consume around \qty{0.2}{\watt}.
\end{table}
\paragraph{Code proportions.}
-Comparing the percentages of code required to implement the smart campus functionalities normalises the data and avoids some issues when comparing \gls{SLOC} for different programming languages, and especially for languages with different paradigms like object-oriented \gls{PYTHON} and functional \gls{CLEAN}. \Cref{fig_t4t:multipercentage} shows the percentage of the total \gls{SLOC} required to implement the smart campus functionalities in each of the four implementations, and is computed from the data in \cref{table_t4t:multi}. It shows that there are significant differences between the percentage of code for each functionality between the tiered and tierless implementations. For example 17\% of the tiered implementations specifies communication, whereas this requires only 3\% of the tierless implementations, i.e.\ 6$\times$ less. We explore the reasons for this in \cref{sec_t4t:Communication}. The other major difference is the massive percentage of Database Interface code in the tierless implementations: at least 47\%. The smart campus specification required a standard DBMS, and the \gls{CLEAN}\slash\gls{ITASK} SQL interface occupies some 78 \gls{SLOC}. While this is a little less than the 106 \gls{SLOC} used in \gls{PYTHON} (\cref{table_t4t:multi}), it is a far higher percentage of systems with total codebases of only around 160 \gls{SLOC}. Idiomatic \gls{CLEAN}/\gls{ITASK} would use high level abstractions to store persistent data in an \gls{SDS}, requiring just a few \gls{SLOC}.
+Comparing the percentages of code required to implement the smart campus functionalities normalises the data and avoids some issues when comparing \gls{SLOC} for different programming languages, and especially for languages with different paradigms like object-oriented \gls{PYTHON} and functional \gls{CLEAN}. \Cref{fig_t4t:multipercentage} shows the percentage of the total \gls{SLOC} required to implement the smart campus functionalities in each of the four implementations, and is computed from the data in \cref{table_t4t:multi}. It shows that there are significant differences between the percentage of code for each functionality between the tiered and tierless implementations. For example 17\% of the tiered implementations specifies communication, whereas this requires only 3\% of the tierless implementations, i.e.\ 6$\times$ less. We explore the reasons for this in \cref{sec_t4t:Communication}. The other major difference is the massive percentage of Database Interface code in the tierless implementations: at least 47\%. The smart campus specification required a standard DBMS, and the \gls{CLEAN}\slash\gls{ITASK} SQL interface occupies some 78 \gls{SLOC}. While this is a little less than the 106 \gls{SLOC} used in \gls{PYTHON} (\cref{table_t4t:multi}), it is a far higher percentage of systems with total codebases of only around 160 \gls{SLOC}. Idiomatic \gls{CLEAN}/\gls{ITASK} would use high level abstractions to store persistent data in \pgls{SDS}, requiring just a few \gls{SLOC}.
The total size of \gls{CWS} and \gls{CRS} would be reduced by a factor of two and the percentage of Database Interface code would be even less than in the tiered \gls{PYTHON} implementations.
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