Update figure 8 to add sensorsNumber==1

.#2019-ICA3PP.org

@@ -0,0 +1 @@
+loic@lguegan.29280:1563296642

2019-ICA3PP.org

@@ -1072,8 +1072,8 @@ applicability of our model.
      [[file:plots/numberSensors-WIFINET.png]]
 
 
-    Final plot: Energy cloud, network and sensors
-    #+BEGIN_SRC R :noweb yes :results graphics :file plots/final.png
+    Final plot: Energy cloud, network and sensors 
+    #+BEGIN_SRC R :noweb yes :results  graphics :file plots/final.png
       <<RUtils>>
 
       # Linear Approx
@@ -1097,10 +1097,12 @@ applicability of our model.
       data=data%>%filter(state=="sim",simKey=="nbSensors")
 
       # Cloud
+      data=data%>%mutate(sensorsNumber=nbSensors)
       data20=data%>%filter(nbSensors==20)%>%mutate(energy=mean(energy)) %>% slice(1L)
       data100=data%>%filter(nbSensors==100)%>%mutate(energy=mean(energy)) %>% slice(1L)
       data300=data%>%filter(nbSensors==300)%>%mutate(energy=mean(energy)) %>% slice(1L)
       dataCloud=rbind(data20,data100,data300)%>%mutate(sensorsNumber=nbSensors)%>%mutate(type="Cloud")%>%select(sensorsNumber,energy,type)
+      dataCloud=bind_rows(dataCloud,tibble(sensorsNumber=1,energy=approx(data20,data100,1),type="Cloud"))
       dataCloud=dataCloud%>%mutate(energy=energy/7) # Divide by 7 because 14 core so 1 machine can host 14 vm but we use redundancy (2VM for 1app)
 
       # Network
@@ -1109,14 +1111,14 @@ applicability of our model.
       dataN5=data%>%filter(sensorsNumber==5)%>% mutate(energy=networkEnergy) %>%select(energy,sensorsNumber)
       dataN10=data%>%filter(sensorsNumber==10)%>%mutate(energy=networkEnergy) %>%select(energy,sensorsNumber)
       dataNet=rbind(dataN5,dataN10)
-      fakeNet=tibble(sensorsNumber=c(20,100,300))
+      fakeNet=tibble(sensorsNumber=c(1,20,100,300))
       fakeNet=fakeNet%>%mutate(energy=approx(dataN5,dataN10,sensorsNumber),type="Network")
 
       # Sensors
       dataS5=data%>%filter(sensorsNumber==5)%>% mutate(energy=sensorsEnergy) %>%select(energy,sensorsNumber) 
       dataS10=data%>%filter(sensorsNumber==10)%>%mutate(energy=sensorsEnergy) %>%select(energy,sensorsNumber) 
       dataS=rbind(dataS5,dataS10)
-      fakeS=tibble(sensorsNumber=c(20,100,300))
+      fakeS=tibble(sensorsNumber=c(1,20,100,300))
       fakeS=fakeNet%>%mutate(energy=approx(dataS5,dataS10,sensorsNumber),type="Sensors")
 
       # Combine Net/Sensors/Cloud and order factors
@@ -1130,7 +1132,6 @@ applicability of our model.
       xlab("Sensors Number")+ylab("Power 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: