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+library("tidyverse")
+library("class")
+library("rpart")
+library("rpart.plot")
+library("viridis")
+
+## 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=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_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>%
+    mutate(efficiency=energy/coverage)%>%
+    ungroup()
+
+## Prepare data for traning
+set.seed(1) # Reproducibility
+wireless_map=c("lora"=1,"nbiot"=2)
+data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$wireless])#%>%filter(simkey!="hint")
+train_set=data_ml%>%sample_frac(0.8)     # 80% of the data
+test_set=data_ml%>%anti_join(train_set)  # 20% of the data
+
+## KNN training
+knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10)
+## KNN analysis
+knn_cont_table=table(knn_predictions,test_set$simkey)
+knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100)
+knn_prop_table=round(prop.table(knn_cont_table),digits=2)
+
+## Decision tree
+tree=rpart(
+    simkey ~ wireless + wakeupfor + energy + coverage,
+    data=train_set,
+    method="class",
+    minsplit=60,
+    minbucket=1)
+tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class")
+tree_cont_table=table(tree_predictions,test_set$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)
+
+## Prints
+print(paste0("Accuracy: KNN=",knn_accuracy,"% CART=",tree_accuracy,"%"))
+pdf("figures/tree.pdf")
+tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48)))
+silent_call=dev.off()
+## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy
+
+## Generate simulation inputs
+inputs=tibble(
+wakeupfor = c(60,180,60,180),
+wireless  = c("lora", "lora", "nbiot", "nbiot"))
+constraints=apply(inputs,1,function(row){
+    wi=row["wireless"]
+    wa=as.numeric(row["wakeupfor"])
+    ## First extract energy/coverage boundaries
+    min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
+    max_energy=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
+    min_coverage=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
+    max_coverage=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
+    ## Generate random points (10 per scenarios)
+    n=10
+    current_inputs=tibble(
+        wireless=rep(wi,n),
+        wakeupfor=rep(wa,n),
+        energy_constraint=runif(n,min_energy,max_energy),
+        coverage_constraint=round(runif(n,min_coverage,max_coverage)))
+    predictions_knn=knn(train=train_set%>%select(-simkey),test=current_inputs%>%
+                                              rename(energy=energy_constraint,coverage=coverage_constraint)%>%
+                                              mutate(wireless=wireless_map[wireless]),cl=train_set$simkey,k=10)
+    predictions_tree=predict(tree,newdata=current_inputs%>%
+                                              rename(energy=energy_constraint,coverage=coverage_constraint)%>%
+                                              mutate(wireless=wireless_map[wireless]),type="class")
+    knn_final=tibble(cbind(current_inputs,tibble(simkey=predictions_knn,model="knn")))
+    tree_final=tibble(cbind(current_inputs,tibble(simkey=predictions_tree,model="tree")))
+    rbind(knn_final,tree_final)
+})
+inputs=do.call("rbind",constraints)
+## Dimension Energy/Coverage
+ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
+       aes(coverage,energy,color=simkey))+geom_point()+
+    geom_point(data=inputs%>%mutate(wakeupfor=as.character(wakeupfor)),aes(coverage_constraint,energy_constraint),size=3,pch=5)+
+    ggtitle("Dimension Energy/Coverage")+xlab("Coverage")+ylab("Sum of nodes energy consumption (J)")+
+    facet_wrap(~wakeupfor+wireless,scale="free")
+ggsave("figures/random_inputs.pdf")
+write.csv(inputs,"../inputs.csv",row.names=FALSE)