Minor changes

22 files changed, 1252 insertions, 61 deletions

diff --git a/analysis/days.R b/analysis/days.R
index 0ecbc59..6ad721e 100644
--- a/analysis/days.R
+++ b/analysis/days.R
@@ -1,4 +1,5 @@
 library("tidyverse")
+options(dplyr.summarise.inform = FALSE)
 library("class")
 library("rpart")
 library("rpart.plot")
@@ -25,55 +26,62 @@ nsimkey=4
 nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200
 
 ## 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)
-data_seed_isSender=data%>%group_by(simkey,wireless,wakeupfor,seed,isSender)%>%summarize(energy_mean=mean(energy))%>%
-    left_join(tmp_data_coverage,by=c("simkey","wireless","wakeupfor","seed"))%>%
-    mutate(efficiency=energy_mean/coverage)%>%
-    ungroup()
+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))%>%
     mutate(efficiency=energy/coverage)%>%
     ungroup()
 
+F1_Score2=function(truth, pred){
+    result=sapply(c("baseline","extended","hint","hintandextended"),function(c){
+        cur_truth=truth[truth==c]
+        cur_pred=pred[truth==c]
+        col=paste0("f1_",c)
+        score=F1_Score(cur_truth,cur_pred)
+        if(is.nan(score)){score=0}
+        list(tibble(!!col:=score))
+    })
+    do.call("cbind",result)
+}
+
 generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl="lora",wuf=180) {
     attempts=seq(1,attempts_max)
     results=sapply(attempts,function(attempt){
-    ## Prepare data for traning
-    set.seed(1+attempt) # Reproducibility
-    wireless_map=c("lora"=1,"nbiot"=2)
-    cur_data_seed=data_seed%>%filter(wakeupfor==wuf,wireless==wrl)
-    data_ml=cur_data_seed%>%select(-efficiency)%>%mutate(wireless=wireless_map[cur_data_seed$wireless])
-    if(ignore_hint){
-        data_ml=data_ml%>%filter(simkey!="hint")
-    }
-    train_set=data_ml%>%filter(seed<=seed_max)%>%select(-seed)     # train data on seed_max*3 days
-    test_set=data_ml%>%anti_join(train_set)%>%select(-seed)        # build test_sed excluding training set
-
-    ## KNN training
-    knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=min(10,NROW(train_set)))
-    ## KNN analysis
-    knn_cont_table=table(knn_predictions,test_set$simkey)
-    knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100)
-    knn_prop_table=round(prop.table(knn_cont_table),digits=2)
-    knn_f1_score=F1_Score(test_set$simkey,knn_predictions)
-    knn_recall=Recall(test_set$simkey,knn_predictions)
-    knn_precision=Precision(test_set$simkey,knn_predictions)
+        ## Prepare data for traning
+        set.seed(1+attempt) # Reproducibility
+        wireless_map=c("lora"=1,"nbiot"=2)
+        cur_data_seed=data_seed%>%filter(wakeupfor==wuf,wireless==wrl)
+        data_ml=cur_data_seed%>%select(-efficiency)%>%mutate(wireless=wireless_map[cur_data_seed$wireless])
+        if(ignore_hint){
+            data_ml=data_ml%>%filter(simkey!="hint")
+        }
+        train_set=data_ml%>%filter(seed<=seed_max)%>%select(-seed)     # train data on seed_max*3 days
+        test_set=data_ml%>%suppressMessages(anti_join(train_set))%>%select(-seed)        # build test_sed excluding training set
+
+        ## KNN training
+        knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=min(10,NROW(train_set)))
+        ## KNN analysis
+        knn_cont_table=table(knn_predictions,test_set$simkey)
+        knn_accuracy=(sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))
+        knn_prop_table=round(prop.table(knn_cont_table),digits=2)
+        knn_f1_score=F1_Score2(test_set$simkey,knn_predictions)
         
-    ## Decision tree
-    tree=rpart(
-        simkey ~ wireless + wakeupfor + energy + coverage,
-        data=train_set,
-        method="class",
-        minsplit=60,
-        minbucket=1)
-    tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class")
-    tree_cont_table=table(tree_predictions,test_set$simkey)
-    tree_accuracy=round((sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))*100)
-    tree_prop_table=round(prop.table(tree_cont_table),digits=2)
-    tree_f1_score=F1_Score(test_set$simkey,tree_predictions)
-    tree_recall=Recall(test_set$simkey,tree_predictions)
-    tree_precision=Precision(test_set$simkey,tree_predictions)
-     list(tibble(seed_max=seed_max,model=c("knn","tree"),accuracy=c(knn_accuracy,tree_accuracy),f1_score=c(knn_f1_score,tree_f1_score),recall=c(knn_recall,tree_recall),precision=c(knn_precision,tree_precision)))
+        ## Decision tree
+        tree=rpart(
+            simkey ~ wireless + wakeupfor + energy + coverage,
+            data=train_set,
+            method="class",
+            minsplit=60,
+            minbucket=1)
+        tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class")
+        tree_cont_table=table(tree_predictions,test_set$simkey)
+        tree_accuracy=(sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))
+        tree_prop_table=round(prop.table(tree_cont_table),digits=2)
+        tree_f1_score=F1_Score2(test_set$simkey,tree_predictions)
+
+        ## Format data
+        result_data=tibble(seed_max=seed_max,model=c("knn","tree"),accuracy=c(knn_accuracy,tree_accuracy))
+        result_data=cbind(result_data,rbind(knn_f1_score,tree_f1_score))
+        list(result_data)
     })
     ## Prints
     results=do.call("rbind",results)
@@ -81,7 +89,7 @@ generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl
 
 }
 
-generate_accuracy = function(wireless,wakeupfor,steps=1, accuracy=20,ignore_hint=TRUE){
+generate_accuracy = function(wireless,wakeupfor,steps=10, accuracy=10,ignore_hint=TRUE){
     npolicies=4
     if(ignore_hint){npolicies=npolicies-1}
     ## Generate inputs
@@ -94,36 +102,43 @@ generate_accuracy = function(wireless,wakeupfor,steps=1, accuracy=20,ignore_hint
 }
 
 # 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))
+#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(
         mean_accuracy=mean(accuracy),sd_accuracy=sd(accuracy),min_accuracy=min(accuracy),max_accuracy=max(accuracy),
-        mean_f1_score=mean(f1_score),sd_f1_score=sd(f1_score),min_f1_score=min(f1_score),max_f1_score=max(f1_score),
-        mean_recall=mean(recall),sd_recall=sd(recall),min_recall=min(recall),max_recall=max(recall),
-        mean_precision=mean(precision),sd_precision=sd(precision),min_precision=min(precision),max_precision=max(precision))
+        mean_f1_baseline=mean(f1_baseline),sd_f1_baseline=sd(f1_baseline),min_f1_baseline=min(f1_baseline),max_f1_baseline=max(f1_baseline),
+        mean_f1_hint=mean(f1_hint),sd_f1_hint=sd(f1_hint),min_f1_hint=min(f1_hint),max_f1_hint=max(f1_hint),
+        mean_f1_extended=mean(f1_extended),sd_f1_extended=sd(f1_extended),min_f1_extended=min(f1_extended),max_f1_extended=max(f1_extended),
+        mean_f1_hintandextended=mean(f1_hintandextended),sd_f1_hintandextended=sd(f1_hintandextended),min_f1_hintandextended=min(f1_hintandextended),max_f1_hintandextended=max(f1_hintandextended))
 
 ## Result max
 metrics_peak=result_summary%>%group_by(wireless,wakeupfor,model)%>%
-    summarize(max_accuracy=max(mean_accuracy),
-              max_f1_score=max(mean_f1_score),
-              max_recall=max(mean_recall),
-              max_precision=max(mean_precision))
+    summarize(max_accuracy=max(mean_accuracy))
 
 ## Plot
 sapply(c("knn","tree"),function(grp){
-    ggplot(result_summary%>%filter(model==grp),aes(months,mean_accuracy))+
+    data=result_summary%>%filter(model==grp)
+    plot=ggplot(data,aes(months,mean_accuracy))+
         geom_ribbon(aes(ymin=mean_accuracy-sd_accuracy,ymax=mean_accuracy+sd_accuracy),alpha=0.2,color=NA)+
-        geom_line(size=1.1)+geom_point(size=3)+xlab("Number of training months")+ylab(paste("Mean",grp,"accuracy"))+ggtitle(paste(grp,"accuracy"))+
-#        ylim(c(0,100))+
-        geom_hline(data=metrics_peak%>%filter(model==grp),aes(yintercept=max_accuracy),color="red",size=1)+
-        geom_text(data=metrics_peak%>%filter(model==grp),x=0,aes(y=max_accuracy,label = round(max_accuracy,digits=1),vjust=-1),color="red")+
-        facet_wrap(~wireless+wakeupfor)
-        scale_x_continuous(breaks = seq(0, max(result_summary$months), by = 1))
-    ggsave(paste0("figures/months_",grp,".pdf"))
+        geom_line(size=1.1)+geom_point(size=3)+
+        geom_point(data=data%>%drop_na(mean_f1_baseline),aes(months,mean_f1_baseline,color="baseline"))+geom_line(data=data%>%drop_na(mean_f1_baseline),aes(months,mean_f1_baseline,color="baseline"))+
+        geom_point(data=data%>%drop_na(mean_f1_extended),aes(months,mean_f1_extended,color="extended"))+geom_line(data=data%>%drop_na(mean_f1_extended),aes(months,mean_f1_extended,color="extended"))+
+        geom_point(data=data%>%drop_na(mean_f1_hintandextended),aes(months,mean_f1_hintandextended,color="hintandextended"))+geom_line(data=data%>%drop_na(mean_f1_hintandextended),aes(months,mean_f1_hintandextended,color="hintandextended"))
+
+    if(any(!is.na(data$mean_f1_hint))){
+        plot=plot+geom_point(data=data%>%drop_na(mean_f1_hint),aes(months,mean_f1_hint,color="hint"))+geom_line(data=data%>%drop_na(mean_f1_hint),aes(months,mean_f1_hint,color="hint"))
+    }
+
+    plot=plot+xlab("Number of training months")+ylab(paste("Mean",grp,"accuracy"))+ggtitle(paste(grp,"accuracy"))+
+#        geom_hline(data=metrics_peak%>%filter(model==grp),aes(yintercept=max_accuracy),color="red",size=1)+
+#        geom_text(data=metrics_peak%>%filter(model==grp),x=0,aes(y=max_accuracy,label = round(max_accuracy,digits=1),vjust=-1),color="red")+
+        facet_wrap(~wireless+wakeupfor)+scale_x_continuous(breaks = seq(0, max(result_summary$months), by = 1))+ylim(c(0,1))+labs(color="F1 Score")
+    ggsave(paste0("figures/months_",grp,".pdf"),width=20,height=15)
+    print(plot)
 })
 
diff --git a/analysis/figures/combined.pdf b/analysis/figures/combined.pdf
index 931baf7..d9cbbb8 100644
--- a/analysis/figures/combined.pdf
+++ b/analysis/figures/combined.pdf
diff --git a/analysis/figures/dimension_coverage.pdf b/analysis/figures/dimension_coverage.pdf
index eb1555f..a108e34 100644
--- a/analysis/figures/dimension_coverage.pdf
+++ b/analysis/figures/dimension_coverage.pdf
diff --git a/analysis/figures/dimension_efficiency.pdf b/analysis/figures/dimension_efficiency.pdf
index 1a20947..df0eae8 100644
--- a/analysis/figures/dimension_efficiency.pdf
+++ b/analysis/figures/dimension_efficiency.pdf
diff --git a/analysis/figures/dimension_energy-coverage-policy.pdf b/analysis/figures/dimension_energy-coverage-policy.pdf
index 3c77539..d0c73e8 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 8b587f7..beacaa9 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 8d11244..9bd624a 100644
--- a/analysis/figures/dimension_energy-coverage.pdf
+++ b/analysis/figures/dimension_energy-coverage.pdf
diff --git a/analysis/figures/dimension_energy.pdf b/analysis/figures/dimension_energy.pdf
index 1f0490f..379af96 100644
--- a/analysis/figures/dimension_energy.pdf
+++ b/analysis/figures/dimension_energy.pdf
diff --git a/analysis/figures/months_knn.pdf b/analysis/figures/months_knn.pdf
index bee0cd2..17a4ad1 100644
--- a/analysis/figures/months_knn.pdf
+++ b/analysis/figures/months_knn.pdf
diff --git a/analysis/figures/months_tree.pdf b/analysis/figures/months_tree.pdf
index 57129d6..83d4f6d 100644
--- a/analysis/figures/months_tree.pdf
+++ b/analysis/figures/months_tree.pdf
diff --git a/analysis/figures/sim_dimension_coverage_NO_HINT.pdf b/analysis/figures/sim_dimension_coverage_NO_HINT.pdf
index 72182ea..a946b86 100644
--- a/analysis/figures/sim_dimension_coverage_NO_HINT.pdf
+++ b/analysis/figures/sim_dimension_coverage_NO_HINT.pdf
diff --git a/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf b/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf
index 6bfc50c..0f42067 100644
--- a/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf
+++ b/analysis/figures/sim_dimension_coverage_WITH_HINT.pdf
diff --git a/analysis/figures/sim_dimension_energy_NO_HINT.pdf b/analysis/figures/sim_dimension_energy_NO_HINT.pdf
index 4de63fe..0f32541 100644
--- a/analysis/figures/sim_dimension_energy_NO_HINT.pdf
+++ b/analysis/figures/sim_dimension_energy_NO_HINT.pdf
diff --git a/analysis/figures/sim_dimension_energy_WITH_HINT.pdf b/analysis/figures/sim_dimension_energy_WITH_HINT.pdf
index e190d1f..7e411e5 100644
--- a/analysis/figures/sim_dimension_energy_WITH_HINT.pdf
+++ b/analysis/figures/sim_dimension_energy_WITH_HINT.pdf
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new file mode 100644
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+++ b/slides_recap/main.aux
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+\documentclass{beamer}
+
+%Information to be included in the title page:
+\title{Nodes data dissemination policy prediction under energy consumption and network coverage constraints}
+\author{Loic Guegan, Issam Raïs}
+\institute{UiT}
+\date{2022}
+
+
+\setbeamertemplate{navigation symbols}{}
+\begin{document}
+
+\frame{\titlepage}
+
+\begin{frame}
+  \frametitle{Context}
+  Disseminate data:
+  \begin{itemize}
+      \setlength\itemsep{0.8em}
+  \item Wireless communications
+  \item Energy constraint
+  \item Harsh environment
+  \end{itemize}
+  \vspace{1cm}
+  \centering
+  \textbf{But energy and coverage not deterministic}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Problem}
+  \includegraphics[trim={0 10cm 30cm 0},scale=0.3,clip]{../analysis/figures/dimension_energy-coverage.pdf}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Problem}
+  \textbf{How to choose the right configuration for a given setup?}\\
+  \vspace{1cm}
+  Knowing that:\\ \vspace{0.3cm}
+  \begin{itemize}
+  \item Perfect coverage not required:
+    \begin{itemize}
+    \item Just N+1,...,N+n redundancy (so n network coverage)
+    \item Collaboration of n nodes (say 3 to monitor some metrics)
+    \end{itemize}
+  \item Clear nodes energy budget
+  \end{itemize}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Problem}
+  Knowing:
+  \begin{itemize}
+  \item Nodes setup:
+    \begin{itemize}
+    \item LoRa/NbIoT?
+    \item Uptimes duration 60s/180s?
+    \end{itemize}
+  \item Requirements:
+    \begin{itemize}
+    \item Nodes energy consumption
+    \item Network coverage
+    \end{itemize}
+  \end{itemize}
+  \vspace{1cm}
+  \centering
+  \textbf{How can we predict which policy to use to meet the requirements?}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Solution}
+  \begin{centering}
+    \textbf{Use classification models!}
+  \end{centering}\\ \vspace{1cm}
+  We choose:
+  \begin{itemize}
+  \item K Nearrest Neighbours
+  \item Decision Trees
+  \end{itemize}
+  \vspace{1cm}
+  Two ML training approaches:
+  \begin{itemize}
+  \item Offline
+  \item Online
+  \end{itemize}
+\end{frame}
+
+
+\begin{frame}
+  \frametitle{Offline: The hint case}
+  \centering
+  \includegraphics[scale=0.35]{../analysis/figures/dimension_energy-coverage.pdf}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Offline: Coverage}
+  \centering
+  \includegraphics[trim={0 0 20cm 0},scale=0.4,clip]{../analysis/figures/sim_dimension_coverage_WITH_HINT.pdf}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Offline: Energy}
+  \centering
+  \includegraphics[trim={0 0 20cm 0},scale=0.4,clip]{../analysis/figures/sim_dimension_energy_WITH_HINT.pdf}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Online}
+  \centering
+  \includegraphics[scale=0.3]{../analysis/figures/months_knn.pdf}
+\end{frame}
+
+\begin{frame}
+  \frametitle{Contribution}
+  \begin{itemize}
+  \item Methodology
+  \item An offline model
+  \item A vision on how such model ML on an online ML scenario
+  \end{itemize}
+\end{frame}
+
+
+
+\end{document}
diff --git a/slides_recap/main.toc b/slides_recap/main.toc
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