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+library("tidyverse")
+library("class")
+
+## 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 knn
+set.seed(1) # Reproducibility
+data_seed=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=as.numeric(as.factor(data_seed$wireless)))
+
+## Train
+train_set=data_seed%>%sample_frac(0.8)     # 80% of the data
+test_set=data_seed%>%anti_join(train_set)  # 20% of the data
+classifier=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10)
+
+## Analysis
+cont_table=table(classifier,test_set$simkey)
+accuracy=round((sum(diag(cont_table)/sum(rowSums(cont_table))))*100)
+prop_table=round(prop.table(cont_table),digits=2)
+print(prop_table)
+print(paste0("Overall KNN accuracy ",accuracy,"%"))