Clean repo, update paper

1 files changed, 504 insertions, 34 deletions

diff --git a/2019-Mascots.org b/2019-Mascots.org
index a306a16..c562424 100644
--- a/2019-Mascots.org
+++ b/2019-Mascots.org
@@ -2,9 +2,11 @@
 
 #+EXPORT_EXCLUDE_TAGS: noexport
 #+STARTUP: hideblocks
+#+PROPERTY: header-args :eval no
 #+OPTIONS: H:5 author:nil email:nil creator:nil timestamp:nil skip:nil toc:nil ^:nil
 #+LATEX_CLASS: IEEEtran
 #+LATEX_HEADER: \usepackage{hyperref}
+#+LATEX_HEADER: \usepackage{booktabs}
 #+LATEX_HEADER: \IEEEoverridecommandlockouts 
 #+LATEX_HEADER: \author{\IEEEauthorblockN{1\textsuperscript{st} Anne-Cécile Orgerie}
 #+LATEX_HEADER: \IEEEauthorblockA{\textit{Univ Rennes, Inria, CNRS, IRISA, Rennes, France} \\
@@ -35,6 +37,7 @@ component, formatting, style, styling, insert
 * Use-Case [1 col]
 ** Application Characteristic
 
+   #+BEGIN_COMMENT
    The IoT part is composed of an Access Point (AP), connected to several sensors using WIFI. In the
    system, the IoT part is considered as the part where the system data are created. In fact, the
    data life cycle start when the sensors records their respective local temperature at a frequency
@@ -44,67 +47,72 @@ component, formatting, style, styling, insert
 
    The network part is considered as the medium that link the IoT part to the cloud. It is composed
    of several network switches and router and it is considered to be a wired network.
-   
+
+   #+END_COMMENT
+
+      
 ** Cloud Infrastructure
 
 * System Model [2 col]
+
   The system model is divided in two parts. First, the IoT and the Network part are models through
   simulations. Then, the Cloud part is model using real servers connected to watt-meters. In this way,
   it is possible to evaluate the end-to-end energy consumption of the system.
 
 ** IoT Part
    In the first place, the IoT part is composed of several sensors connected to an AP which forms a
-   cell. It is model using the ns-3 network simulator. Thus, we setup between 5 and 20 sensors
-   connected to the AP using WIFI 5GHz 802.11n. The node are placed randomly in a square of 30
-   meters around the AP which correspond to a typical real use case. All the nodes of the cell are
-   setup with the default WIFI energy model provided by ns-3.  The different energy values used by
-   the energy model come from the literature and are provided on Table \ref{tab:wifi-energy}. Note
-   that we suppose that the energy source of the cell nodes are unlimited and thus every nodes can
-   communicate for all the simulation duration.
+   cell. It is model using the ns-3 network simulator. Thus, we setup between 5 and 15 sensors
+   connected to the AP using WIFI 5GHz 802.11n. The node are placed randomly in a rectangle of 400m2
+   around the AP which correspond to a typical real use case. All the nodes of the cell are setup
+   with the default WIFI energy model provided by ns-3.  The different energy values used by the
+   energy model are provided on Table \ref{tab:wifi-energy}. These energy were extracted from
+   previous work\cite{halperin_demystifying_nodate,li_end--end_2018} on 802.11n. Note that we
+   suppose that the energy source of the cell nodes are unlimited and thus every nodes can
+   communicate until the end of all the simulations.
 
    As a scenario, sensors send to the AP packets of 192 bits that include: \textbf{1)} A 128 bits
    sensors id \textbf{2)} A 32 bits integer representing the temperature \textbf{3)} An integer
    timestamp representing the temperature sensing time. The data are transmitted immediately at each
-   sensing interval $I$ varied from 1s to 60s. Finally, the AP is in charge to relay data to the
+   sensing interval $I$ varied from 1s to 60s. Finally, the AP is in charge of relaying data to the
    cloud using the network part.
    
-   
    #+BEGIN_EXPORT latex
    \begin{table}[]
-     \centering
-     \caption{Wifi Energy Settings}
-     \label{tab:wifi-energy}
-     \begin{tabular}{|l|l|l|}
-       \hline
-       & Value  & Reference(s) \\ \hline
-       Supply Voltage & 3.3V   & TODO         \\ \hline
-       Tx             & 0.38A  & TODO         \\ \hline
-       Rx             & 0.313A & TODO         \\ \hline
-       Idle           & 0.273A & TODO         \\ \hline
-     \end{tabular}
-   \end{table}  
+   \centering
+   \caption{Wifi Energy Values}
+   \label{tab:wifi-energy}
+   \begin{tabular}{@{}lr@{}}
+   Parameter      & Value  \\ \midrule
+   Supply Voltage & 3.3V   \\
+   Tx             & 0.38A  \\
+   Rx             & 0.313A \\
+   Idle           & 0.273A \\ \bottomrule
+   \end{tabular}
+   \end{table}
    #+END_EXPORT
 
-
 ** Network Part
    The network part represents the network starting from the AP to the Cloud excluding the server.
    It is also model into ns-3. We consider the server to be 9 hops aways from the AP with a typical
-   round-trip latency of 100ms from the AP to the server. ECOFEN \cite{orgerie_ecofen:_2011} is used
-   to model the energy consumption of the network part. ECOFEN is a ns-3 network energy module for
-   ns-3 dedicated to wired network energy estimation. The different energy values used in the
-   network part are from the literature and shown in Table \ref{tab:net-energy}.
+   round-trip latency of 100ms from the AP to the server. Each node from the AP to the Cloud is
+   assume to be network switches with static and dynamic network energy consumption. ECOFEN
+   \cite{orgerie_ecofen:_2011} is used to model the energy consumption of the network part. ECOFEN
+   is a ns-3 network energy module for ns-3 dedicated to wired network energy estimation. It is
+   based on an energy-per-bit model including static consumption by assuming a linear relation
+   between the amount of data sent to the network interface and the power consumption. The different
+   energy values used to instanciate the ECOFEN energy model for the network part are shown in Table
+   \ref{tab:net-energy} and come from previous work \cite{cornea_studying_2014-1}.
 
    #+BEGIN_EXPORT latex
    \begin{table}[]
      \centering
      \caption{Network Part Energy Settings}
      \label{tab:net-energy}
-     \begin{tabular}{|l|l|l|}
-       \hline
-       & Value  & Reference(s) \\ \hline
-       Idle & 1J   & TODO         \\ \hline
-       Bytes (Tx/Rx)             & 3.4nJ  & TODO         \\ \hline
-       Pkt (Tx/Rx)             & 192.0nJ & TODO         \\ \hline
+     \begin{tabular}{@{}lr@{}}
+       Parameter  & Value \\ \midrule
+       Idle       & 1J            \\ 
+       Bytes (Tx/Rx)  & 3.4nJ           \\ 
+       Pkt (Tx/Rx)    & 192.0nJ         \\ \bottomrule
      \end{tabular}
    \end{table}  
    #+END_EXPORT
@@ -113,8 +121,16 @@ component, formatting, style, styling, insert
    Finally, to measure the energy consumption of the server, we used real server from the
    large-scale test-beds Grid5000 (G5K). In fact, G5K has a cluster called Nova composed of several
    nodes which are connected to watt-meters. In this way, we can benefit from real energy
-   measurements. The server is configured to use KVM for virtualization. Virtual Machines (VM)
+   measurements. The server used in the experiment is composed of Intel Xeon E5-2620 processor with
+   64 GB of RAM and 600GB of disk space on a Linux based distribution. This server is configured to
+   use KVM as virtualization mechanism. We deploy a classical Linux x86_64 disctribution on the
+   Virtual Machines (VM) along with a MySQL database. We different amount of allocated memory for
+   the VM namely 1024MB/2048MB/4096MB to highlight its effects on the server energy consumption.
 
+   The sensors requests are simulated using another server. This server is in charge to send hundred
+   of requests to the VM in order to fill the database. Consequently, it is easy to vary the
+   different requests characteristics namely: \textbf{1)} The number request, to virtually
+   add/remove sensors \textbf{2)} The requests frequency.
 
 * Evaluation [3 col]
 ** IoT/Network Consumption
@@ -129,9 +145,463 @@ component, formatting, style, styling, insert
 \bibliographystyle{IEEEtran}
 \bibliography{references}
 
+
+* Data Provenance :noexport:
+
+** Data Analysis
+*** NS3
+    To Generate all the plots, please execute the following line:
+    #+NAME: runAnalysis
+    #+CALL: plotToPDF(plots=genAllPlots(data=NS3-logToCSV()))
+
+    #+RESULTS: runAnalysis
+
+**** R Scripts
+***** Generate all plots script
+      Available variables:
+      |---------------------|
+      | Name                |
+      |---------------------|
+      | sensorsSendInterval |
+      | sensorsPktSize      |
+      | sensorsNumber       |
+      | nbHop               |
+      | linksBandwidth      |
+      | linksLatency        |
+      | totalEnergy         |
+      | nbPacketCloud       |
+      | nbNodes             |
+      | avgDelay            |
+      | simKey              |
+      |---------------------|
+
+      #+NAME: genAllPlots
+      #+BEGIN_SRC R :noweb yes :results output
+        <<NS3-RUtils>>
+        data=read_csv("logs/ns3/last/data.csv")
+  #      easyPlotGroup("linksLatency","totalEnergy", "LATENCY","sensorsNumber")
+  #      easyPlotGroup("linksBandwidth","totalEnergy", "BW","sensorsNumber")
+        easyPlot("sensorsNumber","totalEnergy", "NBSENSORS")
+        easyPlotGroup("positionSeed", "totalEnergy","SENSORSPOS","sensorsNumber")
+        easyPlotGroup("positionSeed", "avgDelay","SENSORSPOS","sensorsNumber")
+        easyPlotGroup("sensorsSendInterval","sensorsEnergy","SENDINTERVAL","sensorsNumber")
+        easyPlotGroup("sensorsSendInterval","networkEnergy","SENDINTERVAL","sensorsNumber")
+
+      #+END_SRC
+
+      #+RESULTS: genAllPlots
+
+***** R Utils
+      RUtils is intended to load logs (data.csv) and providing
+      simple plot function for them.
+
+      #+NAME: NS3-RUtils
+      #+BEGIN_SRC R :eval never
+        library("tidyverse")
+
+        # Fell free to update the following
+        labels=c(nbNodes="Number of nodes",sensorsNumber="Number of sensors",totalEnergy="Total Energy (J)",
+          nbHop="Number of hop (AP to Cloud)", linksBandwidth="Links Bandwidth (Mbps)", avgDelay="Average Application Delay (s)",
+          linksLatency="Links Latency (ms)", sensorsSendInterval="Sensors Send Interval (s)", positionSeed="Position Seed",
+          sensorsEnergy="Sensors Wifi Energy Consumption (J)", networkEnergy="Network Energy Consumption (J)")
+
+        # Get label according to varName
+        getLabel=function(varName){
+          if(is.na(labels[varName])){
+            return(varName)
+          }
+          return(labels[varName])
+        }
+
+        easyPlot=function(X,Y,KEY){
+          curData=data%>%filter(simKey==KEY)
+          stopifnot(NROW(curData)>0)
+          ggplot(curData,aes_string(x=X,y=Y))+geom_point()+geom_line()+xlab(getLabel(X))+ylab(getLabel(Y))
+          ggsave(paste0("plots/",KEY,"-",X,"_",Y,".png"))
+        }
+
+        easyPlotGroup=function(X,Y,KEY,GRP){
+          curData=data%>%filter(simKey==KEY) %>% mutate(!!GRP:=as.character(UQ(rlang::sym(GRP)))) # %>%mutate(sensorsNumber=as.character(sensorsNumber))
+          stopifnot(NROW(curData)>0)
+          ggplot(curData,aes_string(x=X,y=Y,color=GRP,group=GRP))+geom_point()+geom_line()+xlab(getLabel(X))+ylab(getLabel(Y)) + labs(color = getLabel(GRP))
+          ggsave(paste0("plots/",KEY,"-",X,"_",Y,".png"))
+        }
+      #+END_SRC
+
+**** Plots -> PDF
+     Merge all plots in plots/ folder into a pdf file.
+     #+NAME: plotToPDF
+     #+BEGIN_SRC bash :results output :noweb yes
+       orgFile="plots/plots.org"
+       <<singleRun>> # To get all default arguments
+
+       # Write helper function
+       function write {
+           echo "$1" >> $orgFile
+       }
+
+       echo "#+TITLE: Analysis" > $orgFile
+       write "#+LATEX_HEADER: \usepackage{fullpage}"
+       write "#+OPTIONS: toc:nil"
+       # Default arguments
+       write '\begin{center}'
+       write '\begin{tabular}{lr}'
+       write 'Parameters & Values\\'
+       write '\hline'
+       write "sensorsPktSize & ${sensorsPktSize} bytes\\\\"
+       write "sensorsSendInterval & ${sensorsSendInterval}s\\\\"
+       write "sensorsNumber & ${sensorsNumber}\\\\"
+       write "nbHop & ${nbHop}\\\\"
+       write "linksBandwidth & ${linksBandwidth}Mbps\\\\"
+       write "linksLatency & ${linksLatency}ms\\\\"
+       write '\end{tabular}'
+       write '\newline'
+       write '\end{center}'
+
+       for plot in $(find plots/ -type f -name "*.png")
+       do
+           write "\includegraphics[width=0.5\linewidth]{$(basename ${plot})}"
+       done
+
+       # Export to pdf
+       emacs $orgFile --batch -f org-latex-export-to-pdf --kill  
+     #+END_SRC
+
+     #+RESULTS:
+
+**** Log -> CSV
+     logToCSV extract usefull data from logs and put them into logs/data.csv.
+
+     #+NAME: NS3-logToCSV
+     #+BEGIN_SRC bash :results none
+       csvOutput="logs/data.csv"
+
+       # First save csv header line
+       aLog=$(find logs/ -type f -name "*.org"|head -n 1)
+       metrics=$(cat $aLog|grep "\-METRICSLINE\-"|sed "s/-METRICSLINE-//g")
+       echo $metrics | awk '{for(i=1;i<=NF;i++){split($i,elem,":");printf(elem[1]);if(i<NF)printf(",");else{print("")}}}' > $csvOutput
+
+       # Second save all values
+       for logFile in $(find logs/ -type f -name "*.org")
+       do
+           metrics=$(cat $logFile|grep "\-METRICSLINE\-"|sed "s/-METRICSLINE-//g")
+           echo $metrics | awk '{for(i=1;i<=NF;i++){split($i,elem,":");printf(elem[2]);if(i<NF)printf(",");else{print("")}}}' >> $csvOutput
+       done
+     #+END_SRC
+
+
+
+   
+   
+   
+**** Custom Plots
+
+     #+NAME: ssiNet
+     #+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsSendInterval-net.png
+       <<NS3-RUtils>>
+       # Load Data
+        data=read_csv("logs/ns3/last/data.csv")
+
+       data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==20) %>% ggplot(aes(x=sensorsSendInterval,y=networkEnergy))+xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("networkEnergy"))+
+       geom_line()+labs(title="For 20 sensors")
+       ggsave("plots/sensorsSendInterval-net.png",dpi=80)
+     #+END_SRC
+
+   
+     #+NAME: ssiWifi
+     #+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsSendInterval-wifi.png
+       <<NS3-RUtils>>
+       data=read_csv("logs/ns3/last/data.csv")
+       data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==20) %>% ggplot(aes(x=sensorsSendInterval,y=sensorsEnergy))+xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("sensorsEnergy"))+
+       geom_line() +     geom_line()+labs(title="For 20 sensors")
+       ggsave("plots/sensorsSendInterval-wifi.png",dpi=80)
+     #+END_SRC
+
+
+      #+BEGIN_SRC R :results graphics  :noweb yes  :file plot-final.png :session *R*
+        <<NS3-RUtils>>
+        simTime=1800
+
+        # Network
+        data=read_csv("logs/ns3/last/data.csv")
+        data=data%>%filter(simKey=="NBSENSORS")
+        dataC5=data%>%filter(sensorsNumber==5)%>% mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
+        dataC10=data%>%filter(sensorsNumber==10)%>%mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
+        dataNet=rbind(dataC5,dataC10)%>%mutate(type="Network")
+
+        # Network
+        dataS5=data%>%filter(sensorsNumber==5)%>% mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber) 
+        dataS10=data%>%filter(sensorsNumber==10)%>%mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber) 
+        dataS=rbind(dataS5,dataS10)%>%mutate(type="Sensors")
+      
+        data=rbind(dataNet,dataS)%>%mutate(sensorsNumber=as.character(sensorsNumber))
+
+        ggplot(data)+geom_bar(aes(x=sensorsNumber,y=energy,fill=type),stat="identity")+xlab("Sensors Number")+ylab("Power Consumption (W)")+guides(fill=guide_legend(title="Part"))
+        ggsave("plot-final.png",dpi=80)
+
+      #+END_SRC
+
+   
+*** Cloud
+**** R Scripts
+***** Plots script
+      #+BEGIN_SRC R :results output  :noweb yes  :file second-final/plot.png
+        <<RUtils>>
+        dataOrig=loadData("./second-final/data.csv")
+
+        data=dataOrig%>%filter(simKey=="nbSensors")%>%filter(state=="sim",nbSensors==100)
+        dataIDLE=dataOrig%>%filter(simKey=="nbSensors")%>%filter(state!="sim",nbSensors==100)
+        data=data%>%mutate(meanEnergy=mean(energy))
+        dataIDLE=dataIDLE%>%mutate(meanEnergy=mean(energy))
+        data=rbind(data,dataIDLE)
+        ggplot(data,aes(x=time,y=energy))+geom_point(position="jitter")+xlab(getLabel("time"))+expand_limits(y=0)+facet_wrap(~state)+geom_hline(aes(color=state,yintercept=mean(meanEnergy)))
+        ggsave("./second-final/plot.png",dpi=180)
+      #+END_SRC
+
+      #+RESULTS:
+      #+begin_example
+      # A tibble: 3,050 x 8
+                  ts energy simKey    vmSize nbSensors   time state meanEnergy
+               <dbl>  <dbl> <chr>      <dbl>     <dbl>  <dbl> <chr>      <dbl>
+       1 1558429001.   90.2 nbSensors   2048       100 0      IDLE        90.8
+       2 1558429001.   89   nbSensors   2048       100 0.0199 IDLE        90.8
+       3 1558429001.   89   nbSensors   2048       100 0.0399 IDLE        90.8
+       4 1558429001.   90.8 nbSensors   2048       100 0.0599 IDLE        90.8
+       5 1558429001.   91   nbSensors   2048       100 0.0799 IDLE        90.8
+       6 1558429001.   90.5 nbSensors   2048       100 0.1000 IDLE        90.8
+       7 1558429001.   89.9 nbSensors   2048       100 0.120  IDLE        90.8
+       8 1558429001.   88.6 nbSensors   2048       100 0.140  IDLE        90.8
+       9 1558429001.   88.6 nbSensors   2048       100 0.160  IDLE        90.8
+      10 1558429001.   90.5 nbSensors   2048       100 0.180  IDLE        90.8
+      # … with 3,040 more rows
+      #+end_example
+
+
+
+****** Custom Plots
+
+     #+NAME: ssiNet
+     #+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsSendInterval-net.png
+       <<RUtils>>
+
+       data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==20) %>% ggplot(aes(x=sensorsSendInterval,y=networkEnergy))+xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("networkEnergy"))+
+       geom_line()+labs(title="For 20 sensors")
+       ggsave("plots/sensorsSendInterval-net.png",dpi=80)
+     #+END_SRC
+
+     #+RESULTS:
+     [[file:plots/sensorsSendInterval-net.png]]
+
+   
+     #+NAME: ssiWifi
+     #+BEGIN_SRC R :noweb yes :results graphics :file plots/sensorsSendInterval-wifi.png
+       <<RUtils>>
+       data%>%filter(simKey=="SENDINTERVAL",sensorsNumber==20) %>% ggplot(aes(x=sensorsSendInterval,y=sensorsEnergy))+xlab(getLabel("sensorsSendInterval"))+ylab(getLabel("sensorsEnergy"))+
+       geom_line() +     geom_line()+labs(title="For 20 sensors")
+       ggsave("plots/sensorsSendInterval-wifi.png",dpi=80)
+     #+END_SRC
+
+     #+RESULTS: ssiWifi
+     [[file:plots/sensorsSendInterval-wifi.png]]
+
+     #+RESULTS:
+     [[file:plots/sensorsSendInterval.png]]
+
+
+***** R Utils
+      RUtils is intended to load logs (data.csv) and providing
+      simple plot function for them.
+
+      #+NAME: G5K-RUtils
+      #+BEGIN_SRC R :eval never
+        library("tidyverse")
+
+        # Fell free to update the following
+        labels=c(time="Time (s)")
+   
+        loadData=function(path){
+          data=read_csv(path)
+        }
+
+        # Get label according to varName
+        getLabel=function(varName){
+          if(is.na(labels[varName])){
+            return(varName)
+          }
+          return(labels[varName])
+        }
+      #+END_SRC
+    
+**** Plots -> PDF
+     Merge all plots in plots/ folder into a pdf file.
+     #+NAME: plotToPDF
+     #+BEGIN_SRC bash :results output :noweb yes
+       orgFile="plots/plots.org"
+       <<singleRun>> # To get all default arguments
+
+       # Write helper function
+       function write {
+           echo "$1" >> $orgFile
+       }
+
+       echo "#+TITLE: Analysis" > $orgFile
+       write "#+LATEX_HEADER: \usepackage{fullpage}"
+       write "#+OPTIONS: toc:nil"
+       # Default arguments
+       write '\begin{center}'
+       write '\begin{tabular}{lr}'
+       write 'Parameters & Values\\'
+       write '\hline'
+       write "sensorsPktSize & ${sensorsPktSize} bytes\\\\"
+       write "sensorsSendInterval & ${sensorsSendInterval}s\\\\"
+       write "sensorsNumber & ${sensorsNumber}\\\\"
+       write "nbHop & ${nbHop}\\\\"
+       write "linksBandwidth & ${linksBandwidth}Mbps\\\\"
+       write "linksLatency & ${linksLatency}ms\\\\"
+       write '\end{tabular}'
+       write '\newline'
+       write '\end{center}'
+
+       for plot in $(find plots/ -type f -name "*.png")
+       do
+           write "\includegraphics[width=0.5\linewidth]{$(basename ${plot})}"
+       done
+
+       # Export to pdf
+       emacs $orgFile --batch -f org-latex-export-to-pdf --kill  
+     #+END_SRC
+
+**** CSVs -> CSV
+     Merge all energy file into one (and add additional fields).
+
+     #+NAME: G5K-mergeCSV
+     #+BEGIN_SRC sh
+       #!/bin/bash
+
+       whichLog="second-final"
+
+
+       logFile="$(dirname $(readlink -f $0))"/$whichLog/simLogs.txt
+       dataFile=$(dirname "$logFile")/data.csv
+
+
+       getValue () {
+           line=$(echo "$1" | grep "Simulation para"|sed "s/Simulation parameters: //g")
+           key=$2
+           echo "$line"|awk 'BEGIN{RS=" ";FS=":"}"'$key'"==$1{gsub("\n","",$0);print $2}'
+       }
+
+       ##### Add extract info to energy #####
+       IFS=$'\n'
+       for cmd in $(cat $logFile|grep "Simulation parameters")
+       do
+           nodeName=$(getValue $cmd serverNodeName)
+           from=$(getValue $cmd simStart)
+           to=$(getValue $cmd simEnd)
+           vmSize=$(getValue $cmd vmSize)
+           nbSensors=$(getValue $cmd nbSensors)
+           simKey=$(getValue $cmd simKey)
+           csvFile="$whichLog/${simKey}_${vmSize}VMSIZE_${nbSensors}NBSENSORS_${from}${to}.csv"
+           csvFileIDLE="$whichLog/${simKey}_${vmSize}VMSIZE_${nbSensors}NBSENSORS_${from}${to}_IDLE.csv"
+           tmpFile=${csvFile}_tmp
+           echo ts,energy,simKey,vmSize,nbSensors,time,state > $tmpFile
+           minTs=$(tail -n+2 $csvFile|awk -F"," 'BEGIN{min=0}$1<min||min==0{min=$1}END{print(min)}') # To compute ts field
+           minTsIDLE=$(tail -n+2 $csvFileIDLE|awk -F"," 'BEGIN{min=0}$1<min||min==0{min=$1}END{print(min)}') # To compute ts field
+           tail -n+2 ${csvFile} | awk -F"," '{print $0",'$simKey','$vmSize','$nbSensors',"$1-'$minTs'",sim"}' >> $tmpFile
+           tail -n+2 ${csvFileIDLE} | awk -F"," '{print $0",'$simKey','$vmSize','$nbSensors',"$1-'$minTsIDLE'",IDLE"}' >> $tmpFile
+       done
+
+
+       ##### Fill dataFile #####
+       echo ts,energy,simKey,vmSize,nbSensors,time,state > $dataFile
+       for tmpFile in $(find ${whichLog}/*_tmp -type f)
+       do
+           tail -n+2 $tmpFile >> $dataFile
+           rm $tmpFile # Pay attention to this line :D
+       done
+     #+END_SRC
+
+     #+RESULTS: mergeCSV
+*** Final Plots
+
+    
+     #+BEGIN_SRC R :noweb yes :results graphics :file plots/final.png :session *R*
+       library("tidyverse")
+
+       # Load data
+       data=read_csv("./logs/g5k/second-final/data.csv")
+       data=data%>%filter(state=="sim",simKey=="nbSensors")
+
+       # Cloud
+       data10=data%>%filter(nbSensors==20)%>%mutate(energy=mean(energy)) %>% slice(1L)
+       data100=data%>%filter(nbSensors==100)%>%mutate(energy=mean(energy)) %>% slice(1L)
+       data300=data%>%filter(nbSensors==300)%>%mutate(energy=mean(energy)) %>% slice(1L)
+       dataCloud=rbind(data10,data100,data300)%>%mutate(sensorsNumber=nbSensors)%>%mutate(type="Cloud")%>%select(sensorsNumber,energy,type)
+
+
+
+       approx=function(data1, data2,nbSensors){
+         x1=data1$sensorsNumber
+         y1=data1$energy
+
+         x2=data2$sensorsNumber
+         y2=data2$energy
+
+         a=((y2-y1)/(x2-x1))
+         b=y1-a*x1
+
+         return(a*nbSensors+b)
+
+       }
+
+
+       simTime=1800
+
+       # Network
+       data=read_csv("./logs/ns3/last/data.csv")
+       data=data%>%filter(simKey=="NBSENSORS")
+       dataC5=data%>%filter(sensorsNumber==5)%>% mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
+       dataC10=data%>%filter(sensorsNumber==10)%>%mutate(energy=networkEnergy/simTime) %>%select(energy,sensorsNumber)
+       dataNet=rbind(dataC5,dataC10)%>%mutate(type="Network")
+
+       # Sensors
+       dataS5=data%>%filter(sensorsNumber==5)%>% mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber) 
+       dataS10=data%>%filter(sensorsNumber==10)%>%mutate(energy=sensorsEnergy/simTime) %>%select(energy,sensorsNumber) 
+       dataS=rbind(dataS5,dataS10)%>%mutate(type="Sensors")
+
+       fakeNetS=tibble(
+         sensorsNumber=c(20,100,300,20,100,300),
+         energy=c(dataC10$energy,approx(dataC5,dataC10,100),approx(dataC5,dataC10,300),dataS10$energy,approx(dataS5,dataS10,100),approx(dataS5,dataS10,300)),
+         type=c("Network","Network","Network","Sensors","Sensors","Sensors")
+       )
+
+       fakeNetS=fakeNetS%>%mutate(sensorsNumber=as.character(sensorsNumber))
+       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)))
+
+       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"))
+       ggsave("plots/final.png",dpi=80)
+
+  #+END_SRC
+
+  #+RESULTS:
+  [[file:plots/final.png]]
+
+
+
+
+
 * Emacs settings :noexport:
   # Local Variables:
   # eval:    (unless (boundp 'org-latex-classes) (setq org-latex-classes nil))
   # eval:    (add-to-list 'org-latex-classes
   #                       '("IEEEtran" "\\documentclass[conference]{IEEEtran}\n \[NO-DEFAULT-PACKAGES]\n \[EXTRA]\n"  ("\\section{%s}" . "\\section*{%s}") ("\\subsection{%s}" . "\\subsection*{%s}")                       ("\\subsubsection{%s}" . "\\subsubsection*{%s}")                       ("\\paragraph{%s}" . "\\paragraph*{%s}")                       ("\\subparagraph{%s}" . "\\subparagraph*{%s}")))
   # End:
+
+
+