Add plots+update paper

.#2019-Mascots.org

@@ -0,0 +1 @@
+loic@lguegan.1106:1558519162

2019-Mascots.org

@@ -242,7 +242,7 @@ component, formatting, style, styling, insert
    and database accesses. Figure \ref{fig:sensorsFrequency} present the impact on the server energy
    consumption of changing the send interval of 50 sensors to 1s, 10s and 30s. We can see that, the
    more sensors send interval is low, the more server energy consumption peaks occurs. Therefore, it
-   leads to an increase in the server energy consumption.
+   leads to an increasing in the server energy consumption.
 
    #+BEGIN_EXPORT latex
    \begin{figure}
@@ -253,9 +253,31 @@ component, formatting, style, styling, insert
    \end{figure}
    #+END_EXPORT
    
-   
-   
 ** End-To-End Consumption
+   
+   To have an overview of the energy consume by the system, it is important to consider the
+   end-to-end energy consumption. The Figure \ref{fig:end-to-end} represent the end-to-end system
+   energy consumption while varying the number of sensors. It is important to see that, for
+   small-scale systems, the server energy consumption is dominant face to the energy consumed by the
+   sensors. However, since we are using a single server, large-scale sensors deployment lead to an
+   increasing consumtion of energy in the sensors side. On the other side, network energy
+   consumption is stable regarding to the number of sensors that are deployed since network the
+   system use case do not required large data transfert. Thus, it is important to remember that, to
+   save energy, we should maximize the number of sensors handle by each cloud server while keeping a
+   resonable sensors requests intervals.
+
+   #+BEGIN_EXPORT latex
+   \begin{figure}
+     \centering
+     \hspace{1cm}
+     \includegraphics[scale=0.3]{plots/final.png}
+     \label{fig:end-to-end}
+     \caption{End-to-end network energy consumption using sensors interval of 10s}
+   \end{figure}
+   #+END_EXPORT
+   
+
+
 * Discussion [1 col]
 * Conclusion [1 col]
 * References [1 col]
@@ -629,8 +651,8 @@ component, formatting, style, styling, insert
 
 
   Final plot: Energy cloud, network and sensors
-  #+BEGIN_SRC R :noweb yes :results graphics :file plots/final.png :session *R*
-    library("tidyverse")
+  #+BEGIN_SRC R :noweb yes :results output graphics :file plots/final.png
+    <<RUtils>>
 
     # Load data
     data=loadData("./logs/g5k/last/data.csv")
@@ -681,12 +703,11 @@ component, formatting, style, styling, insert
     dataCloud=dataCloud%>%mutate(sensorsNumber=as.character(sensorsNumber))
     data=rbind(fakeNetS,dataCloud)%>%mutate(sensorsNumber=as.character(sensorsNumber))
     data=data%>%mutate(sensorsNumber=fct_reorder(sensorsNumber,as.numeric(sensorsNumber)))
-
+    data$type=factor(data$type,ordered=TRUE,levels=c("Sensors","Network","Cloud"))
     p=ggplot(data)+geom_bar(position="dodge2",colour="black",aes(x=sensorsNumber,y=energy,fill=type),stat="identity")+ 
-    xlab("Sensors Number")+ylab("Power Consumption (W)")+guides(fill=guide_legend(title="Part"))
-    p=applyTheme(p)
-    ggsave("plots/final.png",dpi=80)
-
+    xlab("Sensors Number")+ylab("Energy Consumption (W)")+guides(fill=guide_legend(title="System Part"))
+    p=applyTheme(p)+theme(text = element_text(size=16))
+    ggsave("plots/final.png",dpi=90,width=8,height=5.5)
   #+END_SRC
 
   #+RESULTS: