Update

10 files changed, 204 insertions, 25 deletions

diff --git a/analysis/analysis.R b/analysis/analysis.R
index 8a76849..bd27626 100644
--- a/analysis/analysis.R
+++ b/analysis/analysis.R
@@ -1,4 +1,5 @@
 library("tidyverse")
+library("viridis")
 
 ## Simulation Parameters:
 ## simkey         {baseline,extended,hint,hintandextended}
@@ -19,7 +20,7 @@ nwireless=2
 nsimkey=4
 nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200
 
-if(1==2){
+if(FALSE){ # Toggle
 ## Load data
 data=read_csv("../CCGRID2022.csv")%>%distinct() # Note that in the data experiment wireless=="lora",seed==1,wakeupfor==60,simkey=="baseline" is present 2 times in the CSV file
 tmp_data_coverage=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%mutate(coverage=sum(nDataRcv))%>%ungroup()%>%filter(isSender==1)%>%select(simkey,wireless,wakeupfor,seed,coverage)
@@ -42,24 +43,17 @@ sim=sim%>%mutate(id_rand=rand_id[id])
 }
 ## Perform various plots to understand the data
 GG_VARIOUS=function(){
-    ## Dimension Coverage
-    ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
-           aes(simkey,coverage,color=wakeupfor))+geom_point()+facet_wrap(~wireless+wakeupfor)+ggtitle("Dimension Coverage")
-    ggsave("figures/dimension_coverage.pdf")
-    ## Dimension Energy
-    ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
-           aes(simkey,energy,color=wakeupfor))+geom_point()+facet_wrap(~wireless+wakeupfor)+ggtitle("Dimension Energy")+ylab("Energy Consumption (J)")
-    ggsave("figures/dimension_energy.pdf")
-    ## Dimension Efficiency
-    ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
-           aes(simkey,efficiency,color=wakeupfor))+
-        geom_point()+facet_wrap(~wireless+wakeupfor)+ggtitle("Dimension Efficiency")+ylab("Efficiency (J/Succ)")
-    ggsave("figures/dimension_efficiency.pdf")
     ## Dimension Energy/Coverage
     ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
-           aes(coverage,energy,color=simkey))+geom_point(alpha=0.5)+labs(color="Policy")+theme(legend.position="top")+
-        ggtitle("Dimension Energy/Coverage")+xlab("Coverage")+ylab("Nodes energy consumption (J)")+
-        facet_wrap(~wakeupfor+wireless,scale="free")
+           aes(coverage,energy,color=simkey))+geom_point(aes(shape=simkey),alpha=0.5)+labs(color="Policy")+
+        xlab("Network coverage")+ylab("Nodes energy consumption (J)")+
+        scale_shape_manual(values=c(22, 15, 16,1))+labs(shape="Policy",color="Policy")+
+        theme_bw()+
+        guides(colour = guide_legend(override.aes = list(alpha = 1)))+
+        facet_wrap(~wakeupfor+wireless,scale="free")+scale_color_viridis(discrete=TRUE)+
+        theme(legend.position = c(0.15,0.87),
+                         legend.background = element_rect(fill = "white", color = "black",size=0.8))+
+        scale_x_continuous(breaks=seq(0,12))
     ggsave("figures/dimension_energy-coverage.pdf")
     ## Dimension Energy/Coverage Wakeupfor
     ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
@@ -114,8 +108,8 @@ GG_SIMULATIONS = function(file,key="NOKEY") {
 
 }
 
-GG_SIMULATIONS("../simulations/results/results_NoHintIsFALSE.csv",key="WITH_HINT")
-GG_SIMULATIONS("../simulations/results/results_NoHintIsTRUE.csv",key="NO_HINT")
+#GG_SIMULATIONS("../simulations/results/results_NoHintIsFALSE.csv",key="WITH_HINT")
+#GG_SIMULATIONS("../simulations/results/results_NoHintIsTRUE.csv",key="NO_HINT")
 
 system("rm -f figures/combined.pdf")
 system("pdfunite figures/*.pdf figures/combined.pdf")
diff --git a/analysis/days.R b/analysis/days.R
index ce12930..2b671d1 100644
--- a/analysis/days.R
+++ b/analysis/days.R
@@ -107,11 +107,12 @@ generate_accuracy = function(wireless,wakeupfor,steps=10, accuracy=10,ignore_hin
 }
 
 # Generate accuracy for each wireless and uptime
-#accuracy=rbind(generate_accuracy("lora",60),
-#               generate_accuracy("lora",180),
-#               generate_accuracy("nbiot",60),
-#               generate_accuracy("nbiot",180))
-
+if(F){ # Toggle to train
+accuracy=rbind(generate_accuracy("lora",60),
+               generate_accuracy("lora",180),
+               generate_accuracy("nbiot",60),
+               generate_accuracy("nbiot",180))
+}
 ## Summarize
 result_summary=accuracy%>%group_by(wireless,wakeupfor,months,model)%>%
     summarize(
@@ -125,7 +126,41 @@ result_summary=accuracy%>%group_by(wireless,wakeupfor,months,model)%>%
 metrics_peak=result_summary%>%group_by(wireless,wakeupfor,model)%>%
     summarize(max_accuracy=max(mean_accuracy))
 
-## Plot
+
+ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(linetype=model))+
+    geom_line(aes(months,mean_f1_baseline,color="Baseline"),size=1.2)+
+    geom_line(aes(months,mean_f1_extended,color="Extended"),size=1.2)+
+    geom_line(aes(months,mean_f1_hintandextended,color="Hintandextended"),size=1.2)+labs(color="Classes",linetype="Model")+
+    facet_wrap(~wireless+wakeupfor)+
+    scale_x_continuous(breaks = seq(0, 15, by = 1))+
+    scale_y_continuous(breaks = seq(0, 1, by = 0.1))+
+    theme_bw()+
+    theme(panel.grid.minor = element_blank(),
+          legend.position = c(0.9,0.74),
+          legend.margin = margin(2,4,2,4),
+          legend.spacing=unit(-0.2,"cm"),
+          legend.box.margin=margin(1,1,1,1),
+          legend.box.background = element_rect(fill = "white", color = "black",size=0.8))+
+    xlab("Training months")+ylab("Classes F1-Score")+
+    scale_color_viridis(discrete=TRUE,end=0.7)
+ggsave("figures/months_f1-score.pdf",width=8.5,height=6)
+stopifnot(1)
+
+## Plot Merge Accuracy
+ggplot(data=result_summary%>%mutate(model=ifelse(model=="knn","KNN","DT")),aes(months,mean_accuracy,color=model,shape=model))+
+    geom_point(size=3)+geom_line(size=1.2)+xlab("Training months")+ylab("Model accuracy")+labs(color="Models",shape="Models")+
+    scale_x_continuous(breaks = seq(0, 15, by = 1))+
+    scale_y_continuous(breaks = seq(0, 1, by = 0.1))+
+    facet_wrap(~wireless+wakeupfor)+
+    theme_bw()+
+    theme(panel.grid.minor = element_blank(),
+          legend.position = c(0.38,0.68),
+          legend.background = element_rect(fill = "white", color = "black",size=0.8))+
+    scale_color_viridis(discrete=TRUE,end=0.7)
+ggsave("figures/months_accuracy.pdf",width=8.5,height=6)
+
+
+## Plot accuracy + F1-Score
 sapply(c("knn","tree"),function(grp){
     data=result_summary%>%filter(model==grp)
     plot=ggplot(data,aes(months,mean_accuracy))+
diff --git a/analysis/figures/combined.pdf b/analysis/figures/combined.pdf
index 9a4ed4b..4d4b866 100644
--- a/analysis/figures/combined.pdf
+++ b/analysis/figures/combined.pdf
diff --git a/analysis/figures/dimension_energy-coverage-policy.pdf b/analysis/figures/dimension_energy-coverage-policy.pdf
index c0440c7..8798a50 100644
--- a/analysis/figures/dimension_energy-coverage-policy.pdf
+++ b/analysis/figures/dimension_energy-coverage-policy.pdf
diff --git a/analysis/figures/dimension_energy-coverage-wakeupfor.pdf b/analysis/figures/dimension_energy-coverage-wakeupfor.pdf
index 42869c6..77c2b09 100644
--- a/analysis/figures/dimension_energy-coverage-wakeupfor.pdf
+++ b/analysis/figures/dimension_energy-coverage-wakeupfor.pdf
diff --git a/analysis/figures/dimension_energy-coverage.pdf b/analysis/figures/dimension_energy-coverage.pdf
index 6d7cf5c..91c195f 100644
--- a/analysis/figures/dimension_energy-coverage.pdf
+++ b/analysis/figures/dimension_energy-coverage.pdf
diff --git a/analysis/figures/months_accuracy.pdf b/analysis/figures/months_accuracy.pdf
new file mode 100644
index 0000000..51c8440
--- /dev/null
+++ b/analysis/figures/months_accuracy.pdf
diff --git a/analysis/figures/months_f1-score.pdf b/analysis/figures/months_f1-score.pdf
new file mode 100644
index 0000000..42557a8
--- /dev/null
+++ b/analysis/figures/months_f1-score.pdf
diff --git a/analysis/kmeans.R b/analysis/kmeans.R
new file mode 100644
index 0000000..320ba8d
--- /dev/null
+++ b/analysis/kmeans.R
@@ -0,0 +1,51 @@
+########## INFORMATIONS ##########
+# This file is made to study online classification
+# So, each pair (wireless,wakeupfor) has its classification models (knn and decision tree)
+##################################
+
+library("tidyverse")
+options(dplyr.summarise.inform = FALSE)
+library("class")
+library("rpart")
+library("rpart.plot")
+library("viridis")
+library("MLmetrics")
+
+## Simulation Parameters:
+## simkey         {baseline,extended,hint,hintandextended}
+## wireless       {lora,nbiot}
+## wakeupfor      {60s,180s}
+## seed           [1,200]
+## node           on[0,12]
+## isSender       {0,1}
+## dataSize       {1MB}
+
+## Metrics:
+## energy         [0,+inf)
+## nDataRcv       [0,+inf)
+
+nseed=200
+nwakeupfor=2
+nwireless=2
+nsimkey=4
+nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200
+
+## Load data
+data=suppressMessages(read_csv("../CCGRID2022.csv"))%>%distinct() # Note that in the data experiment wireless=="lora",seed==1,wakeupfor==60,simkey=="baseline" is present 2 times in the CSV file
+data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>% ungroup()
+
+## Format data
+data_seed=data_seed%>%filter(simkey!="hint")
+wireless_map=c("lora"=1,"nbiot"=2)
+simkey_map=c("baseline"=1,"hint"=2,"extended"=3,"hintandextended"=4)
+data_seed$wireless=wireless_map[data_seed$wireless]
+data_seed$simkey=simkey_map[data_seed$simkey]
+
+
+## Lora 180s
+data_kmean=data_seed%>%filter(wireless==1,wakeupfor==180)%>%select(-wireless,-wakeupfor,-seed)%>%relocate(simkey)
+m=kmeans(data_kmean,3,nstart=50,iter.max=80)
+data_kmean=data_kmean%>%mutate(cluster=as.factor(m$cluster))
+
+## plots
+ggplot(data=data_kmean,aes(coverage,energy,color=as.factor(cluster)))+geom_point()
diff --git a/analysis/renv.lock b/analysis/renv.lock
index c51eb94..e597486 100644
--- a/analysis/renv.lock
+++ b/analysis/renv.lock
@@ -17,6 +17,14 @@
       "Hash": "b2866e62bab9378c3cc9476a1954226b",
       "Requirements": []
     },
+    "KernSmooth": {
+      "Package": "KernSmooth",
+      "Version": "2.23-20",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "8dcfa99b14c296bc9f1fd64d52fd3ce7",
+      "Requirements": []
+    },
     "MASS": {
       "Package": "MASS",
       "Version": "7.3-57",
@@ -25,6 +33,16 @@
       "Hash": "71476c1d88d1ebdf31580e5a257d5d31",
       "Requirements": []
     },
+    "MLmetrics": {
+      "Package": "MLmetrics",
+      "Version": "1.1.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "c8c93b7ca030b43af9e0394700be4c7e",
+      "Requirements": [
+        "ROCR"
+      ]
+    },
     "Matrix": {
       "Package": "Matrix",
       "Version": "1.4-1",
@@ -51,6 +69,16 @@
       "Hash": "45f0398006e83a5b10b72a90663d8d8c",
       "Requirements": []
     },
+    "ROCR": {
+      "Package": "ROCR",
+      "Version": "1.0-11",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "cc151930e20e16427bc3d0daec62b4a9",
+      "Requirements": [
+        "gplots"
+      ]
+    },
     "askpass": {
       "Package": "askpass",
       "Version": "1.1",
@@ -103,6 +131,14 @@
         "bit"
       ]
     },
+    "bitops": {
+      "Package": "bitops",
+      "Version": "1.0-7",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "b7d8d8ee39869c18d8846a184dd8a1af",
+      "Requirements": []
+    },
     "blob": {
       "Package": "blob",
       "Version": "1.2.3",
@@ -150,6 +186,16 @@
         "sass"
       ]
     },
+    "caTools": {
+      "Package": "caTools",
+      "Version": "1.18.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "34d90fa5845004236b9eacafc51d07b2",
+      "Requirements": [
+        "bitops"
+      ]
+    },
     "cachem": {
       "Package": "cachem",
       "Version": "1.0.6",
@@ -486,6 +532,28 @@
         "vctrs"
       ]
     },
+    "gplots": {
+      "Package": "gplots",
+      "Version": "3.1.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "75437dd4c43599f6e9418ea249495fda",
+      "Requirements": [
+        "KernSmooth",
+        "caTools",
+        "gtools"
+      ]
+    },
+    "gridExtra": {
+      "Package": "gridExtra",
+      "Version": "2.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "7d7f283939f563670a697165b2cf5560",
+      "Requirements": [
+        "gtable"
+      ]
+    },
     "gtable": {
       "Package": "gtable",
       "Version": "0.3.1",
@@ -494,6 +562,14 @@
       "Hash": "36b4265fb818f6a342bed217549cd896",
       "Requirements": []
     },
+    "gtools": {
+      "Package": "gtools",
+      "Version": "3.9.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "32600a0bea839c4d0ac998d7c547ecf7",
+      "Requirements": []
+    },
     "haven": {
       "Package": "haven",
       "Version": "2.5.1",
@@ -739,6 +815,17 @@
         "askpass"
       ]
     },
+    "patchwork": {
+      "Package": "patchwork",
+      "Version": "1.1.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "63b611e9d909a9ed057639d9c3b77152",
+      "Requirements": [
+        "ggplot2",
+        "gtable"
+      ]
+    },
     "pillar": {
       "Package": "pillar",
       "Version": "1.8.1",
@@ -1181,6 +1268,18 @@
         "rlang"
       ]
     },
+    "viridis": {
+      "Package": "viridis",
+      "Version": "0.6.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "ee96aee95a7a563e5496f8991e9fde4b",
+      "Requirements": [
+        "ggplot2",
+        "gridExtra",
+        "viridisLite"
+      ]
+    },
     "viridisLite": {
       "Package": "viridisLite",
       "Version": "0.4.1",