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Loic Guegan 5a77b67d6b Clean repo, update paper
2019-05-22 10:15:45 +02:00

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#+TITLE: Estimating the end-to-end energy consumption of IoT devices along with their impact on Cloud and telecommunication infrastructures
#+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} \\
#+LATEX_HEADER: Rennes, France \\
#+LATEX_HEADER: anne-cecile.orgerie@irisa.fr}
#+LATEX_HEADER: \and
#+LATEX_HEADER: \IEEEauthorblockN{2\textsuperscript{nd} Loic Guegan}
#+LATEX_HEADER: \IEEEauthorblockA{\textit{Univ Rennes, Inria, CNRS, IRISA, Rennes, France} \\
#+LATEX_HEADER: Rennes, France \\
#+LATEX_HEADER: loic.guegan@irisa.fr}
#+LATEX_HEADER: }
#+BEGIN_EXPORT latex
\begin{abstract}
Information and Communication Technology takes a growing part in the worldwide energy consumption. One of the root causes of this increase lies in the multiplication of connected devices. Each object of the Internet-of-Things often does not consume much energy by itself. Yet, their number and the infrastructures they require to properly work have leverage. In this paper, we combine simulations and real measurements to study the energy impact of IoT devices. In particular, we analyze the energy consumption of Cloud and telecommunication infrastructures induced by the utilization of connected devices, and we propose an end-to-end energy consumption model for these devices.
\end{abstract}
\begin{IEEEkeywords}
component, formatting, style, styling, insert
\end{IEEEkeywords}
#+END_EXPORT
* Introduction [2 col]
* Related Work [1 col]
* 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
$f$ and the local timestamp. Then, these data are transmitted through the network along with an
arbitrary sensor id of 128 bits. Finally, the AP is in charge to transmit the data to the cloud
using the network part.
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 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 of relaying data to the
cloud using the network part.
#+BEGIN_EXPORT latex
\begin{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. 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}{@{}lr@{}}
Parameter & Value \\ \midrule
Idle & 1J \\
Bytes (Tx/Rx) & 3.4nJ \\
Pkt (Tx/Rx) & 192.0nJ \\ \bottomrule
\end{tabular}
\end{table}
#+END_EXPORT
** Cloud Part
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 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
** Cloud Energy Consumption
** Virtual Machine Size Impact
** Application Accuracy
Refresh frequency etc...
** End-To-End Consumption
* Discussion [1 col]
* Conclusion [1 col]
* References [1 col]
\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:
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